WO2018152977A1 - 一种图像降噪方法及终端和计算机存储介质 - Google Patents

一种图像降噪方法及终端和计算机存储介质 Download PDF

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Publication number
WO2018152977A1
WO2018152977A1 PCT/CN2017/085512 CN2017085512W WO2018152977A1 WO 2018152977 A1 WO2018152977 A1 WO 2018152977A1 CN 2017085512 W CN2017085512 W CN 2017085512W WO 2018152977 A1 WO2018152977 A1 WO 2018152977A1
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Prior art keywords
noise reduction
current frame
lens
image
frame image
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PCT/CN2017/085512
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English (en)
French (fr)
Inventor
张文娜
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中兴通讯股份有限公司
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Publication of WO2018152977A1 publication Critical patent/WO2018152977A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration using local operators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/80—Camera processing pipelines; Components thereof
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/80—Camera processing pipelines; Components thereof
    • H04N23/81—Camera processing pipelines; Components thereof for suppressing or minimising disturbance in the image signal generation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00—Details of television systems
    • H04N5/14—Picture signal circuitry for video frequency region
    • H04N5/21—Circuitry for suppressing or minimising disturbance, e.g. moiré or halo
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details
    • G06T2207/20032—Median filtering

Definitions

  • the present disclosure relates to image processing technologies in the field of electronic applications, and more particularly to an image noise reduction method and terminal and computer storage medium.
  • Image noise image noise
  • image noise reduction technology has become a problem that cannot be ignored.
  • the image denoising algorithm for image denoising is used to denoise the image, such as median filtering algorithm, adaptive Wiener filtering algorithm, wavelet denoising algorithm and other image denoising algorithms.
  • the image is uniformly processed by an image denoising algorithm according to the noise characteristics and the noise size of the entire image.
  • noise reduction processing due to the imaging characteristics of the lens, the brightness of the image is attenuated from the center of the image to the periphery of the image.
  • the image after noise reduction has the following two extremes: (1) ), the image uses a strong noise reduction processing method, the image has a better noise reduction effect around the image, and the image is clean, but the image center detail sacrifices a lot, the image resolution decreases, and the image is blurred; (2) the image adopts a weaker drop.
  • the noise processing method retains more details of the image as a whole, but the noise around the image is large and the image is not clean. In short, when the image is denoised by the existing noise reduction processing method, the image quality will be insufficient.
  • Embodiments of the present invention are directed to an image denoising method, a terminal, and a computer storage medium, which are capable of differentiating image denoising processing, and improving image quality after image denoising.
  • An embodiment of the present invention provides an image noise reduction method, including:
  • the determining, according to the first lens parameter, the first noise reduction system of the current frame image Number including:
  • N third noise reduction coefficients respectively corresponding to the N first images, and determining the N third noise reduction coefficients as the first corresponding to the current frame image a noise reduction coefficient, wherein the N first images are in one-to-one correspondence with the N third noise reduction coefficients.
  • the determining the N third noise reduction coefficients respectively corresponding to the N first images according to the first lens uniformity characteristic parameter and the first lens parameter comprises:
  • the performing noise reduction processing on the current frame image according to the first noise reduction coefficient comprises:
  • the determining, according to the first lens parameter, the first noise reduction coefficient of the current frame image includes:
  • the first lens parameter comprises: a first lens uniformity characteristic parameter and a first lens uniformity compensation parameter
  • the embodiment of the invention provides a terminal, including:
  • An acquiring unit configured to acquire a current frame image; and acquire a first lens parameter corresponding to the current frame image;
  • a determining unit configured to determine a first noise reduction coefficient of the current frame image according to the first lens parameter
  • noise reduction unit configured to perform noise reduction processing on the current frame image according to the first noise reduction coefficient.
  • the terminal further includes: a dividing unit;
  • the dividing unit is configured to divide the current frame image into N first images corresponding to N regions, where N is a natural number greater than 1;
  • the determining unit is configured to determine, according to the first lens parameter, N third noise reduction coefficients respectively corresponding to the N first images, and determine the N third noise reduction coefficients as the current The first noise reduction coefficient corresponding to the frame image, wherein the N first images are in one-to-one correspondence with the N third noise reduction coefficients.
  • the terminal further includes: a calculating unit and a converting unit;
  • the calculating unit is configured to calculate a first distance between a center point of each first image and a center point of the current frame image
  • the determining unit is further configured to determine, according to the first distance and the first lens parameter, each first gain value corresponding to each of the first images;
  • the converting unit is configured to convert the third noise reduction coefficient corresponding to each of the first images according to each of the first gain values until the N third noise reduction coefficients are obtained.
  • the noise reduction unit is configured to separately perform noise reduction processing on the N first images according to the N third noise reduction coefficients, where the N third noise reductions are performed.
  • the coefficients are in one-to-one correspondence with the N first images.
  • the terminal further includes: a determining unit;
  • the acquiring unit is further configured to acquire a second lens uniformity characteristic parameter corresponding to the image of the previous frame;
  • the determining unit is configured to determine whether there is a difference between the first lens uniformity characteristic parameter and the second lens uniformity characteristic parameter according to a preset tolerance threshold;
  • the first lens parameter includes: a first lens uniformity Characteristic parameters and first lens uniformity compensation parameters;
  • the determining unit is configured to determine, according to the first lens uniformity compensation parameter, a first noise reduction coefficient of the current frame image, or determine if there is no difference, if the difference is determined And determining, by the second noise reduction coefficient corresponding to the image of the previous frame, the second noise reduction coefficient as the first noise reduction coefficient.
  • the embodiment of the invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions are used to perform at least one of the foregoing methods.
  • An embodiment of the present invention provides an image denoising method and a terminal, which acquires a current frame image, acquires a first lens parameter corresponding to a current frame image, and determines a first noise reduction coefficient of the current frame image according to the first lens parameter;
  • the first noise reduction coefficient performs noise reduction processing on the current frame image.
  • FIG. 1 is a flowchart 1 of an image noise reduction method according to an embodiment of the present invention
  • FIG. 2 is a schematic structural diagram of a pixel arrangement manner according to an embodiment of the present invention.
  • FIG. 3 is a schematic diagram of CMOS photon sensing according to an embodiment of the present invention.
  • FIG. 4 is an LS graph of an exemplary 3-frame image according to an embodiment of the present invention.
  • FIG. 5 is an image diagram of an exemplary original RAW data according to an embodiment of the present invention.
  • FIG. 6 is an exemplary LSC characteristic curve corresponding to FIG. 5 according to an embodiment of the present invention.
  • FIG. 7 is a second flowchart of an image noise reduction method according to an embodiment of the present invention.
  • FIG. 8 is a schematic structural diagram 1 of a terminal according to an embodiment of the present disclosure.
  • FIG. 9 is a schematic structural diagram 2 of a terminal according to an embodiment of the present disclosure.
  • FIG. 10 is a schematic structural diagram 3 of a terminal according to an embodiment of the present disclosure.
  • FIG. 11 is a schematic structural diagram 4 of a terminal according to an embodiment of the present disclosure.
  • FIG. 12 is a schematic structural diagram 5 of a terminal according to an embodiment of the present invention.
  • An embodiment of the present invention provides an image noise reduction method. As shown in FIG. 1 , the method may include:
  • the terminal when the terminal photographs the target object by using the photographing function, when the terminal starts the photographing function, the terminal can preview the image corresponding to the target object through the display screen, so the terminal can acquire the current frame image.
  • the current frame image refers to a preview image corresponding to the target object acquired by the terminal in the current frame.
  • the terminal acquires a current frame image corresponding to the target object, and the current frame image is the current frame, in the process of the image capturing or photographing by the terminal.
  • Raw RAW data In the embodiment of the present invention, the acquisition of the original RAW data of the current frame is implemented by an image sensor provided in the terminal. Since the image sensor can acquire the preview image of the target object in real time, that is, the different frame preview images are always obtained during the preview, the embodiment of the present invention describes the current frame image acquired by the image sensor, and the principle of each frame image is the same. .
  • the image sensor may include a charge coupled device (CCD) image sensor and a Color Filter Array (CMOS) filter element of a complementary metal oxide semiconductor (CMOS) image sensor.
  • CMOS complementary metal oxide semiconductor
  • the image sensor is a two-dimensional matrix composed of photosensitive elements densely arranged in two directions, forming a Bayer mode RGB pixel dot arrangement, and (a), (b), which are commonly shown in FIG. (c), (d) Four Bayer patterns are arranged, and each CCD corresponds to one pixel. Among them, R induces red light, G senses green light, B induces blue light, and in Bayer mode, G is twice as large as R and B (because human eyes are more sensitive to green).
  • each CCD or CMOS in the above two-dimensional matrix is only used to sense the energy of photons, and each CCD or CMOS generates a corresponding proportion of charges due to the intensity of incoming light, and each CCD or CMOS will The charge information is collected and amplified and stored.
  • the CMOS sensor photons shown in the CMOS photon sensing diagram shown in FIG. 3 finally store the corresponding proportion of charges (blocks shown in FIG. 3), and RAW records each.
  • the charge value of the pixel position, which does not record any color information, that is, the original RAW data of the target object is only a grayscale file.
  • the terminal can perform shooting of the target object through the camera disposed thereon, and in the shooting process, due to the imaging characteristics of the lens, as shown in FIG. 4, different frame images acquired by the terminal (FIG. 4 is
  • the brightness of the three brightness curves of the three-frame image has different degrees of attenuation from the center of the image to the ideal brightness curve around the image.
  • lens uniformity that is, a lens shading curve.
  • the original RAW data when presented in the form of an image, there is no color in a grayscale image mode. In the image formed by the original RAW data as shown in FIG. 5, the image formed by the original RAW data can be seen as black and white. And there is also a mosaic phenomenon (ie noise).
  • the terminal needs to perform image processing (ie, image signal processing) on the RAW data for luminance compensation and noise reduction (image signal processing).
  • image processing ie, image signal processing
  • the terminal can perform image signal processing (ISP, Image Signal Processing) through an image processor.
  • the operation of the terminal on the target object in the embodiment of the present invention may be understood as a process in which an image processing application in the terminal applies a preview image of the image to be processed.
  • the terminal When the terminal performs the image preview, after acquiring the current frame image, the RAW data of the current frame image acquired by the terminal needs to perform image signal processing. Specifically, the terminal may calibrate the brightness attenuation of the current frame image, and adopt a uniform lens.
  • the LSS Lins Shading Correction
  • the first lens parameter includes: a first lens uniformity characteristic parameter and a first lens uniformity compensation parameter.
  • the brightness compensation is performed on different frame images (which can be understood as current frame images at different times), that is, the brightness is performed.
  • Gain processing as shown in FIG. 6, the terminal has a small brightness gain value for the image center of the current frame image (for example, a gain of 1 time), and the gain value around the image of the current frame image is large (for example, a gain of 2 times). Therefore, when the terminal performs ISP processing, the terminal can acquire the first lens uniform characteristic parameter of the current frame image before the LSC is performed, and the first lens uniformity compensation parameter after the LSC is performed. Specifically, the terminal can be obtained by using a lens shading characteristic curve and an LSC characteristic curve (as shown in FIGS. 5 and 6).
  • the terminal in the LSC characteristic curve, can set the ratio of the uniformity of the periphery of the image and the ratio of the uniformity of the image center and the position of the curve inflection point by the parameter, that is, the image after the calibration is not necessarily the central brightness and the four corners.
  • the brightness is exactly the same, but it can also be adjusted. Therefore, the terminal needs to record the lens shading characteristic curve before and after calibration, and obtain the LSC characteristic curve by comparing the lens shading characteristic curve; or directly obtain the LSC characteristic curve from the ISP LSC algorithm, thereby making
  • the first lens uniformity characteristic parameter and the first lens uniformity compensation parameter corresponding to the current frame image are obtained by the terminal in the embodiment of the present invention.
  • the terminal may determine the first noise reduction coefficient of the current frame image in consideration of the first lens parameter.
  • the process of determining, by the terminal, the first noise reduction coefficient of the current frame image according to the first lens parameter may be: the terminal dividing the current frame image into N first images corresponding to the N regions, Wherein, N is a natural number greater than 1; the terminal determines N third noise reduction coefficients corresponding to the N first images according to the first lens parameter (specifically, the first lens uniformity compensation parameter), and the N third descending The noise coefficient is determined as a first noise reduction coefficient corresponding to the current frame image, wherein the N first images are in one-to-one correspondence with the N third noise reduction coefficients.
  • the process of determining, according to the first lens parameter (specifically, the first lens uniformity compensation parameter), the N third noise reduction coefficients corresponding to the N first images respectively is: the terminal calculates the center point of each first image. a first distance from a center point of the current frame image; then, the terminal determines each of the first distance and the first lens parameter Each first gain value corresponding to the first image; finally, the terminal may convert the third noise reduction coefficient corresponding to each first image according to each first gain value until N third noise reduction coefficients are obtained.
  • the LSC characteristic curve of the current frame image indicates the distance between the gain value and the different position of the current frame image from the center of the current frame image.
  • the terminal will be current. After the frame image is divided into N regions, the position of the center point of each region is used as the position of each region in the current frame image. Therefore, the terminal can compensate for the first lens uniformity represented by the first distance and the LSC characteristic curve. a parameter, determining each first gain value corresponding to each first image corresponding to each region, the terminal may obtain N first gain values, each first gain value corresponding to each first image .
  • the terminal divides the image into m*n regions, calculates a first distance between a center point of each region and a center point of the current frame image, and finds a corresponding value in the LSC curve of the current frame image by using a table lookup or a function calculation method.
  • Each first distance is incremented by each first gain value; as follows:
  • the terminal divides the entire image into m*n areas, and the specific areas are as follows:
  • Ration 0,0 ,Ration 0,1 ,Ration 0,2 ,...,Ration m-1,n-1 ,m and n can be set by themselves; calculate the center of the image center of the current frame image The first distance of the center of the area from the center of the image is recorded as: Dis.Ration 0,0 , Dis.Ration 0,1 , Dis.Ration 0,2 ,...,Dis.Ration m-1,n- 1;
  • the terminal finds the gain value added by each region in the LSC algorithm in the LSC curve of the current frame image according to the first distance: gain.Ration 0,0 ,gain.Ration 0,1 ,gain .Ration 0,2 ,...,gain.Ration m-1,n-1 .
  • the look-up table method first sample the LSC curve, obtain a two-dimensional chart (two-dimensional chart of distance and gain values), and then find the corresponding gain.Region in the chart according to each Dis.Region; When Dis.Region is in the middle of two DISs, linear interpolation is used to obtain the gain corresponding to the Dis.
  • the terminal converts the gain value into a noise reduction coefficient: ratio.Ration 0,0 , ratio.Ration 0,1 , ratio.Ration 0,2 ,..., ratio.Ration m-1,n-1 ;
  • the noise reduction level is set according to the degree of noise corresponding to the different gain, and a two-dimensional chart (a two-dimensional chart of gain and ratio) is generated, because the LSC can be used in different brightness environments.
  • Set different correction levels, so 2D charts can be divided into three cases: bright light, normal light and lowlight or more detailed classification.
  • the terminal can find the corresponding ratio.Region in the two-dimensional graph according to each gain.Region; when a ratio.Region is in the middle of the two ratios, the terminal uses linear interpolation to obtain the ratio corresponding to the gain.
  • the terminal can determine the first noise reduction coefficient of the current frame image according to the first lens uniformity compensation parameter.
  • Table 1 is a combination chart of a two-dimensional graph of distance and gain values and a two-dimensional graph of gain and ratio when the gain is 1 time.
  • the terminal may perform noise reduction processing on the current frame image according to the first noise reduction coefficient.
  • the terminal may separately perform noise reduction processing on the N first images according to the N third noise reduction coefficients, wherein the N third noise reduction coefficients are in one-to-one correspondence with the N first images.
  • the terminal may use the obtained first noise reduction coefficient to adjust the noise reduction level of each region corresponding to the current frame image, that is, determine a third noise reduction coefficient of each region, and the noise reduction level may be expressed as: noise. Ration 0,0 , noise.Ration 0,1 , noise.Ration 0,2 &noise.Ration m-1,n-1 .
  • the terminal performs noise reduction processing according to the noise reduction data corresponding to the noise reduction level.
  • the noise reduction coefficient of the current frame image may be adjusted according to the change of the previous frame image, thereby realizing the differentiation of different frame images. Image denoising processing is performed, and image quality is improved by image noise reduction.
  • An embodiment of the present invention provides an image noise reduction method. As shown in FIG. 7, the method may include:
  • the process of “the terminal acquires the current frame image” is the same as that of S101 in the foregoing embodiment. The description is consistent and will not be described here.
  • the process of “the terminal acquiring the first lens parameter corresponding to the current frame image” is consistent with the description of S102 in the foregoing embodiment, and details are not described herein again.
  • S204 Determine, according to a preset tolerance threshold, whether there is a difference between the first lens uniformity characteristic parameter and the second lens uniformity characteristic parameter, where the first lens parameter comprises: a first lens uniformity characteristic parameter and a first lens uniformity compensation parameter.
  • the terminal acquires a second lens uniformity characteristic parameter corresponding to the previous frame image, and determines the current frame according to the preset tolerance threshold, the second lens uniformity characteristic parameter, the first lens uniformity characteristic parameter, and the first lens uniformity compensation parameter.
  • the first noise reduction factor of the image is the first noise reduction factor of the image.
  • the terminal After acquiring the first lens uniformity characteristic parameter and the first lens uniformity compensation parameter (ie, the first lens parameter) corresponding to the current frame image, the terminal acquires the current frame image in real time, and The processing mode of each frame is the same. Therefore, when the terminal acquires the latest current frame image, the terminal can obtain the second lens uniformity characteristic parameter corresponding to the previous frame image in the same manner. In this way, the terminal can determine whether there is a difference between the previous frame image and the current frame image by using the first lens uniformity characteristic parameter, the second lens uniformity characteristic parameter, and the preset tolerance threshold.
  • the terminal may determine, according to the result of determining the difference, whether the first frame noise reduction coefficient is used to obtain the first frame noise reduction coefficient, or the noise reduction coefficient of the previous frame image is used as the first noise reduction coefficient. Noise reduction is performed on the current frame image.
  • the terminal may determine whether there is a difference between the first lens uniformity characteristic parameter and the second lens uniformity characteristic parameter according to the preset tolerance threshold. If it is determined that there is a difference, the terminal determines the first noise reduction coefficient of the current frame image according to the first lens uniformity compensation parameter.
  • the process of determining, by the terminal according to the first lens uniformity compensation parameter, the first noise reduction coefficient of the current frame image may be: the terminal divides the current frame image into N corresponding to the N regions. a first image, wherein N is a natural number greater than 1; the terminal determines N third noise reduction coefficients respectively corresponding to the N first images according to the first lens uniformity compensation parameter, and determines N third noise reduction coefficients as a first noise reduction coefficient corresponding to the current frame image, wherein the N first images are in one-to-one correspondence with the N third noise reduction coefficients.
  • the terminal determines, according to the first lens uniformity compensation parameter, N third noise reduction coefficients corresponding to the N first images respectively
  • the process is specifically: the terminal calculates a first distance between a center point of each first image and a center point of the current frame image; and then, the terminal determines, according to the first distance and the first lens uniformity compensation parameter, that each first image corresponds to Each of the first gain values; finally, the terminal may convert the third noise reduction coefficient corresponding to each of the first images according to each of the first gain values until N third noise reduction coefficients are obtained.
  • the LSC characteristic curve of the current frame image indicates the distance between the gain value and the different position of the current frame image from the center of the current frame image.
  • the terminal will be current. After the frame image is divided into N regions, the position of the center point of each region is used as the position of each region in the current frame image. Therefore, the terminal can compensate for the first lens uniformity represented by the first distance and the LSC characteristic curve. a parameter, determining each first gain value corresponding to each first image corresponding to each region, the terminal may obtain N first gain values, each first gain value corresponding to each first image .
  • the terminal divides the image into m*n regions, calculates a first distance between a center point of each region and a center point of the current frame image, and finds a corresponding value in the LSC curve of the current frame image by using a table lookup or a function calculation method.
  • Each first distance is incremented by each first gain value; as follows:
  • the terminal divides the entire image into m*n areas, and the specific areas are as follows:
  • Ration 0,0 ,Ration 0,1 ,Ration 0,2 ,...,Ration m-1,n-1 ,m and n can be set by themselves; calculate the center of the image center of the current frame image The first distance of the center of the area from the center of the image is recorded as: Dis.Ration 0,0 , Dis.Ration 0,1 , Dis.Ration 0,2 ,...,Dis.Ration m-1,n- 1 ;
  • the terminal finds the gain value added by each region in the LSC algorithm in the LSC curve of the current frame image according to the first distance: gain.Ration 0,0 ,gain.Ration 0,1 ,gain .Ration 0,2 ,...,gain.Ration m-1,n-1 .
  • the look-up table method first sample the LSC curve, obtain a two-dimensional chart (two-dimensional chart of distance and gain values), and then find the corresponding gain.Region in the chart according to each Dis.Region; When Dis.Region is in the middle of two DISs, linear interpolation is used to obtain the gain corresponding to the Dis.
  • the terminal converts the gain value into a noise reduction coefficient: ratio.Ration 0,0 , ratio.Ration 0,1 , ratio.Ration 0,2 ,..., ratio.Ration m-1,n-1 ;
  • the noise reduction level is set according to the degree of noise corresponding to the different gain, and a two-dimensional chart (a two-dimensional chart of gain and ratio) is generated, because the LSC can be used in different brightness environments.
  • Set different correction levels, so 2D charts can be divided into three cases: bright light, normal light and lowlight or more detailed classification.
  • the terminal can find the corresponding ratio.Region in the two-dimensional graph according to each gain.Region; when a ratio.Region is in the middle of the two ratios, the terminal uses linear interpolation to obtain the ratio corresponding to the gain.
  • the terminal can determine the first noise reduction coefficient of the current frame image according to the first lens uniformity compensation parameter.
  • Table 1 is a combination chart of a two-dimensional graph of distance and gain values and a two-dimensional graph of gain and ratio when the gain is 1 time.
  • the terminal may obtain the second lens uniformity compensation parameter corresponding to the previous frame image or obtain the second lens uniformity directly by the algorithm according to the second lens uniformity characteristic parameter.
  • the compensation parameter therefore, the terminal can also determine whether there is a difference between the previous frame image and the current frame image by using the first lens uniformity compensation parameter, the second lens uniformity compensation parameter, and the preset tolerance threshold.
  • the embodiment of the present invention does not limit the manner of determining whether there is a difference between the previous frame image and the current frame image.
  • the terminal may ignore the slight difference or difference of the brightness of the previous frame image and the current frame image. Therefore, setting the preset tolerance threshold to make the difference between the previous frame image and the current frame image is obvious. Case.
  • the preset tolerance threshold may be preset, or may be adjusted according to user requirements, and the preset tolerance threshold may be obtained through experimental results, and may not be set too large or too small, due to If the preset tolerance threshold is set too large, the new noise reduction coefficient will not be determined in real time when the characteristic curve corresponding to the adjacent two frames changes to a certain extent; if the preset tolerance threshold is set too small, the adjacent two frames of data A slight change between them will also re-update the noise reduction coefficient, making the image processing data larger and longer.
  • the terminal After acquiring the first lens parameter corresponding to the current frame image, the terminal acquires in real time The current frame image is processed in the same manner for each frame of the image. Therefore, when the terminal acquires the latest current frame image, the terminal can obtain the second lens uniformity corresponding to the previous frame image in the same manner. Characteristic parameters. In this way, the terminal can determine whether there is a difference between the previous frame image and the current frame image by using the first lens uniformity characteristic parameter, the second lens uniformity characteristic parameter, and the preset tolerance threshold. Then, the terminal may determine, according to the result of determining the difference, whether the first frame noise reduction coefficient is used to obtain the first frame noise reduction coefficient, or the noise reduction coefficient of the previous frame image is used as the first noise reduction coefficient. Noise reduction is performed on the current frame image.
  • the terminal may determine, according to the preset tolerance threshold, whether there is a difference between the first lens uniformity characteristic parameter and the second lens uniformity characteristic parameter. If it is determined that there is no difference, it indicates that the noise reduction coefficient currently used by the terminal, that is, the noise reduction coefficient of the image of the previous frame can be continuously used, that is, the terminal can acquire the second noise reduction coefficient corresponding to the image of the previous frame. And determining the second noise reduction coefficient as the first noise reduction coefficient.
  • the terminal may separately perform noise reduction processing on the N first images according to the N third noise reduction coefficients, wherein the N third noise reduction coefficients and the N first The images correspond one by one.
  • the terminal may use the obtained first noise reduction coefficient to adjust the noise reduction level of each region corresponding to the current frame image, that is, determine a third noise reduction coefficient of each region, and the noise reduction level may be expressed as: noise. Ration 0,0 , noise.Ration 0,1 , noise.Ration 0,2 &noise.Ration m-1,n-1 .
  • the terminal performs noise reduction processing according to the noise reduction data corresponding to the noise reduction level.
  • the noise reduction coefficient of the current frame image may be adjusted according to the change of the previous frame image, thereby realizing the differentiation of different frame images. Image denoising processing is performed, and image quality is improved by image noise reduction.
  • the embodiment of the present invention provides a terminal 1, which may include:
  • the acquiring unit 10 is configured to acquire a current frame image, and acquire a first lens parameter corresponding to the current frame image;
  • the determining unit 11 is configured to determine a first noise reduction coefficient of the current frame image according to the first lens parameter.
  • the noise reduction unit 12 is configured to perform noise reduction processing on the current frame image according to the first noise reduction coefficient.
  • the terminal 1 further includes: a determining unit 13.
  • the acquiring unit is further configured to acquire a second lens uniformity characteristic parameter corresponding to the image of the previous frame.
  • the determining unit 13 is configured to determine whether there is a difference between the first lens uniformity characteristic parameter and the second lens uniformity characteristic parameter according to a preset tolerance threshold, where the first lens parameter comprises: the first lens is uniform sexual characteristic parameters and first lens uniformity compensation parameters.
  • the determining unit 11 is specifically configured to: if it is determined that there is a difference, or if it is determined that there is no difference, acquire a second noise reduction coefficient corresponding to the image of the previous frame, and determine the second noise reduction coefficient Is the first noise reduction coefficient.
  • the terminal 1 further includes: a dividing unit 14.
  • the dividing unit 14 is configured to divide the current frame image into N first images corresponding to N regions.
  • the determining unit 11 is further configured to determine, according to the first lens parameter, N third noise reduction coefficients respectively corresponding to the N first images, and determine the N third noise reduction coefficients as The first noise reduction coefficient corresponding to the current frame image, wherein the N first images are in one-to-one correspondence with the N third noise reduction coefficients.
  • the terminal 1 further includes: a computing unit 15 and a converting unit 16.
  • the calculating unit 15 is configured to calculate a first distance between a center point of each first image and a center point of the current frame image.
  • the determining unit 11 is further configured to determine, according to the first distance and the first lens parameter, each first gain value corresponding to each of the first images.
  • the converting unit 16 is configured to convert the third noise reduction coefficient corresponding to each of the first images according to each of the first gain values until the N third noise reduction coefficients are obtained.
  • the noise reduction unit 12 is configured to separately perform noise reduction processing on the N first images according to the N third noise reduction coefficients, where the N third noise reduction coefficients are The N first images are in one-to-one correspondence.
  • the above-mentioned obtaining unit 10, determining unit 11, noise reducing unit 12, judging unit 13, dividing unit 14, computing unit 15, and converting unit 16 may be processor 17 located on the first terminal.
  • the implementation is specifically implemented by a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA).
  • the first terminal further includes: a storage medium 18, The storage medium 18 can be coupled to the processor 17 via a system bus 19 for storing executable program code, the program code including computer operating instructions, the storage medium 18 may include high speed RAM memory, and may also include non-easy Loss memory, for example, at least one disk storage.
  • the noise reduction coefficient of the current frame image may be adjusted according to the change of the previous frame image, thereby realizing the differentiation of different frame images. Image denoising processing is performed, and image quality is improved by image noise reduction.
  • embodiments of the present invention can be provided as a method, system, or computer program product.
  • embodiments of the invention may take the form of a hardware embodiment, a software embodiment, or a combination of software and hardware aspects.
  • embodiments of the invention may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) including computer usable program code.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.
  • the technical solution provided by the embodiment of the present invention can be applied to the field of image processing.
  • the current frame image is acquired; the first lens parameter corresponding to the current frame image is acquired; and the first noise reduction coefficient of the current frame image is determined according to the first lens parameter;
  • the first noise reduction coefficient performs noise reduction processing on the current frame image.
  • the above scheme is used to implement the technology, because the terminal can calculate the image of each frame based on the lens parameters. Considering the selection of different noise reduction coefficients, the difference of the noise reduction processing for selecting appropriate noise reduction parameters for different frame images is realized, and the image denoising processing is realized by differentiating the image quality.

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Abstract

一种图像降噪方法及终端，该方法包括：获取当前帧图像（S101）；获取与当前帧图像对应的第一镜头参数（S102）；根据第一镜头参数，确定当前帧图像的第一降噪系数（S103）；根据第一降噪系数对当前帧图像进行降噪处理（S104）。

Description

一种图像降噪方法及终端和计算机存储介质 技术领域
本公开涉及电子应用领域中的图像处理技术，尤其涉及一种图像降噪方法及终端和计算机存储介质。
背景技术
随着摄像技术的快速发展和终端的普及，用户对高质量图像的需求不断提高，拍照技术面临着越来越高的要求。而图像噪声(图像噪点)是衡量图像质量的一个重要因素，因此，图像降噪技术成为不可忽视的一个问题。
目前，针对图像降噪采用的图像降噪算法对图像进行降噪处理，例如中值滤波算法、自适应维纳滤波算法、小波降噪算法等图像降噪算法。具体的，根据整个图像的噪点特性和噪点大小采用图像降噪算法统一对该图像进行处理。然而，通过这样的降噪方式的处理，由于镜头的成像特性，图像的亮度从图像的中心到图像的四周有不同程度的衰减，因此，导致降噪后的图像存在以下两种极端：(1)、图像使用较强的降噪处理方式，图像的四周降噪效果较好，图像干净，但是图像中心细节牺牲较大，图像分辨率下降，图像模糊；(2)、图像采用较弱的降噪处理方式，图像整体的细节保留较多，但是图像的四周的噪点大，图像不干净。总之，采用现有的降噪处理方式对图像进行降噪处理时，会导致图像质量存在一定不足。
发明内容
本发明实施例期望提供一种图像降噪方法及终端及计算机存储介质，能够差异化进行图像降噪处理，经图像降噪后提高了图像质量。
本发明实施例的技术方案如下：
本发明实施例提供了一种图像降噪方法，包括：
获取当前帧图像；
获取与所述当前帧图像对应的第一镜头参数；
根据所述第一镜头参数，确定所述当前帧图像的第一降噪系数；
根据所述第一降噪系数对所述当前帧图像进行降噪处理。
在上述方案中，所述根据所述第一镜头参数，确定所述当前帧图像的第一降噪系 数，包括：
将所述当前帧图像划分为N个区域对应的N个第一图像，其中，N为大于1的自然数；
根据所述第一镜头参数，确定所述N个第一图像分别对应的N个第三降噪系数，将所述N个第三降噪系数确定为所述当前帧图像对应的所述第一降噪系数，其中，所述N个第一图像与所述N个第三降噪系数一一对应。
根据一示例性实施例，所述根据所述第一镜头均匀性特性参数和所述第一镜头参数，确定所述N个第一图像分别对应的N个第三降噪系数，包括：
计算每个第一图像的中心点与所述当前帧图像的中心点的第一距离；
根据所述第一距离和所述第一镜头参数，确定所述每个第一图像对应的每个第一增益值；
根据所述每个第一增益值转换成所述每个第一图像对应的第三降噪系数，直至得到所述N个第三降噪系数。
根据一示例性实施例，所述根据所述第一降噪系数对所述当前帧图像进行降噪处理，包括：
根据所述N个第三降噪系数对所述N个第一图像分别进行降噪处理，其中，所述N个第三降噪系数与所述N个第一图像一一对应。
根据一示例性实施例，所述根据所述第一镜头参数，确定所述当前帧图像的第一降噪系数，包括：
获取上一帧图像对应的第二镜头均匀性特性参数；
根据预设容忍阈值，判断第一镜头均匀性特性参数和所述第二镜头均匀性特性参数是否存在差异；所述第一镜头参数包括：第一镜头均匀性特性参数和第一镜头均匀性补偿参数；
若判断出存在差异，则根据所述第一镜头均匀性补偿参数，确定所述当前帧图像的第一降噪系数；
若判断出未存在差异，则获取所述上一帧图像对应的第二降噪系数，将所述第二降噪系数确定为所述第一降噪系数。
本发明实施例提供了一种终端，包括：
获取单元，用于获取当前帧图像；及获取与所述当前帧图像对应的第一镜头参数；
确定单元，用于根据所述第一镜头参数，确定所述当前帧图像的第一降噪系数；
降噪单元，用于根据所述第一降噪系数对所述当前帧图像进行降噪处理。
在上述终端中，所述终端还包括：划分单元；
所述划分单元，用于将所述当前帧图像划分为N个区域对应的N个第一图像，其中，N为大于1的自然数；
所述确定单元，具体用于根据所述第一镜头参数，确定所述N个第一图像分别对应的N个第三降噪系数，将所述N个第三降噪系数确定为所述当前帧图像对应的所述第一降噪系数，其中，所述N个第一图像与所述N个第三降噪系数一一对应。
根据一示例性实施例，所述终端还包括：计算单元和转换单元；
所述计算单元，用于计算每个第一图像的中心点与所述当前帧图像的中心点的第一距离；
所述确定单元，还具体用于根据所述第一距离和所述第一镜头参数，确定所述每个第一图像对应的每个第一增益值；
所述转换单元，用于根据所述每个第一增益值转换成所述每个第一图像对应的第三降噪系数，直至得到所述N个第三降噪系数。
根据一示例性实施例，所述降噪单元，具体用于根据所述N个第三降噪系数对所述N个第一图像分别进行降噪处理，其中，所述N个第三降噪系数与所述N个第一图像一一对应。
根据一示例性实施例，所述终端还包括：判断单元；
所述获取单元，还用于获取上一帧图像对应的第二镜头均匀性特性参数；
所述判断单元，用于根据预设容忍阈值，判断所述第一镜头均匀性特性参数和所述第二镜头均匀性特性参数是否存在差异；所述第一镜头参数包括：第一镜头均匀性特性参数和第一镜头均匀性补偿参数；
所述确定单元，具体用于若判断出存在差异，则根据所述第一镜头均匀性补偿参数，确定所述当前帧图像的第一降噪系数；或者，若判断出未存在差异，则获取所述上一帧图像对应的第二降噪系数，将所述第二降噪系数确定为所述第一降噪系数。
本发明实施例还提供一种计算机存储介质，所述计算机存储介质中存储有计算机可执行指令，所述计算机可执行指令用于执行上述方法中的至少其中之一。
本发明实施例提供了一种图像降噪方法及终端，获取当前帧图像；获取与当前帧图像对应的第一镜头参数；根据第一镜头参数，确定当前帧图像的第一降噪系数；根据第一降噪系数对当前帧图像进行降噪处理。采用上述方案实现技术，由于终端可以 对每帧图像基于镜头参数的考虑进行不同降噪系数的选择，从而实现对不同帧图像选择合适的降噪参数进行降噪处理的差异化，进而通过实现差异化进行图像降噪处理，提高了图像质量。
附图说明
图1为本发明实施例提供的一种图像降噪方法的流程图一；
图2为本发明实施例提供的像素排列方式的结构示意图；
图3为本发明实施例提供的CMOS光子感应示意图；
图4为本发明实施例提供的示例性的3帧图像的LS曲线图；
图5为本发明实施例提供的示例性的原始RAW数据成像图；
图6为本发明实施例提供的示例性的与图5对应的LSC特性曲线图；
图7为本发明实施例提供的一种图像降噪方法的流程图二；
图8为本发明实施例提供的一种终端的结构示意图一；
图9为本发明实施例提供的一种终端的结构示意图二；
图10为本发明实施例提供的一种终端的结构示意图三；
图11为本发明实施例提供的一种终端的结构示意图四；
图12为本发明实施例提供的一种终端的结构示意图五。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述。
实施例一
本发明实施例提供了一种图像降噪方法，如图1所示，该方法可以包括：
S101、获取当前帧图像。
在本发明实施例中，终端通过拍照功能对目标对象进行拍摄时，在该终端启动拍照功能时，该终端通过显示屏可以进行目标对象对应的图像的预览，于是，该终端可以获取当前帧图像，即当前帧的预览图像。其中，当前帧图像是指终端在当前帧获取到的目标对象对应的预览图像。
需要说明的是，在本发明实施例中，在终端利用设置在自身的摄像头进行图像拍摄或拍照的过程中，终端获取目标对象对应的当前帧图像，该当前帧图像为当前帧的 原始RAW数据。在本发明实施例中，当前帧的原始RAW数据的获取是通过设置在终端中的图像传感器实现的。由于图像传感器可以实时的获取目标对象的预览图像，也就是说在预览时一直获取不同帧预览图像，因此，本发明实施例针对图像传感器获取的当前帧图像进行说明，每帧图像的原理都相同。
其中，图像传感器可以包括：电荷耦合元件(CCD,Charge-coupled Device)图像传感器、互补金属氧化物半导体(CMOS，Complementary Metal Oxide Semiconductor)图像传感器的Color Filter Array(CFA彩色滤镜阵列)元件。具体的，上述图像传感器由横竖两个方向密集排列的感光元件组成的一个二维矩阵，构成一种Bayer模式的RGB像素点排列，常见的有图2所示的(a)、(b)、(c)、(d)4种Bayer模式的排列方式，每个CCD就对应一个像素。其中，R感应红光、G感应绿光、B感应蓝光，而在Bayer模式中G是R和B的两倍(因为人类的眼睛对绿色更敏感)。
需要说明的是，在上述二维矩阵内的每个CCD或CMOS只是用来感受光子的能量，每个CCD或CMOS中由于进入光线的强度而产生对应比例的电荷，每个CCD或CMOS将这些电荷信息汇集并经过放大并储存起来，例如，如图3所示的CMOS光子感应示意图所示CMOS感应光子最后存储对应比例的电荷(图3中所示的方块)，而RAW记录的就是每个像素位置的电荷值，它是没有记录任何的颜色信息的，也就是说：目标对象的原始RAW数据只是灰度文件而已。
在本发明实施例中，终端可以通过设置在其上的摄像头进行目标对象的拍摄，而在拍摄过程中，由于镜头的成像特性，如图4所示，终端获取的不同帧图像(图4为3帧图像的3条亮度曲线)的亮度从图像中心到图像四周与理想的亮度曲线会有不同程度的衰减，本领域技术人员将这种现象称为镜头均匀性，即lens shading曲线。
需要说明的是，原始RAW数据以图像形式呈现时则为一个灰度图像模式没有颜色的，如图5所示的原始RAW数据形成的图像中可以看到原始的RAW数据形成的图像是黑白色，并且还有马赛克现象(即噪点)出现。
因此，在本发明实施例中，终端需要将RAW数据进行图像处理(即图像信号处理)进行亮度补偿和降噪(图像信号处理)。终端可以通过图像处理器进行图像信号处理(ISP，Image Signal Processing)。
需要说明的是，本发明实施例中的终端对目标对象的操作可以理解为终端中的一个图像处理应用对待处理图像的预览图像的处理。
S102、获取与当前帧图像对应的第一镜头参数。
终端在进行图像预览时，获取当前帧图像之后，由于该终端获取的当前帧图像的RAW数据是需要进行图像信号处理的，具体的，终端可以对当前帧图像的亮度衰减进行校准，采用镜头均匀性补偿算法(LSC，Lens Shading Correction)进行图像信号处理的亮度衰减，并继续进行降噪过程。
在本发明实施例中，第一镜头参数包括：第一镜头均匀性特性参数和第一镜头均匀性补偿参数。
需要说明的是，本发明实施例中采用LSC对当前帧图像进行图像处理或图像信号处理的时候，是对不同帧图像(可以理解为不同时刻的当前帧图像)进行亮度补偿，即对亮度进行增益处理，如图6所示，终端对当前帧图像的图像中心的亮度增益值较小(例如增益1倍)，而当前帧图像的图像四周的增益值较大(例如，增益2倍)。因此，终端进行ISP处理时，该终端可以获取当前帧图像在进行LSC之前的第一镜头均匀特性参数，以及在进行了LSC之后的第一镜头均匀性补偿参数了。具体的，终端可以通过lens shading特性曲线和LSC特性曲线来获取(如图5和6所示)。
在本发明实施例中，在LSC特性曲线中，终端通过参数可以设置图像四周均匀性的比例和图像中心均匀性的比例，以及曲线拐点的位置，即校准之后的图像并不一定中心亮度和四角亮度完全一致，而是也可以调整的，因此，终端需要记录校准前后的lens shading特性曲线，并通过对比lens shading特性曲线得到LSC特性曲线；或者从ISP LSC算法中直接获取LSC特性曲线，从而使得终端获取了与该当前帧图像对应的第一镜头均匀性特性参数和第一镜头均匀性补偿参数本发明实施例不作限制。
S103、根据第一镜头参数，确定当前帧图像的第一降噪系数。
终端在了获取与当前帧图像对应的第一镜头参数之后，该终端就可以在考虑第一镜头参数的情况下，来确定当前帧图像的第一降噪系数了。
具体的，在本发明实施例中，终端根据第一镜头参数，确定当前帧图像的第一降噪系数的过程可以为：终端将当前帧图像划分为N个区域对应的N个第一图像，其中，N为大于1的自然数；终端根据第一镜头参数(具体为第一镜头均匀性补偿参数)，确定N个第一图像分别对应的N个第三降噪系数，将N个第三降噪系数确定为当前帧图像对应的第一降噪系数，其中，该N个第一图像与N个第三降噪系数一一对应。而终端根据第一镜头参数(具体为第一镜头均匀性补偿参数)，确定N个第一图像分别对应的N个第三降噪系数的过程具体为：终端计算每个第一图像的中心点与当前帧图像的中心点的第一距离；然后，终端根据第一距离和第一镜头参数，确定每个 第一图像对应的每个第一增益值；最后，终端可以根据每个第一增益值转换成每个第一图像对应的第三降噪系数，直至得到N个第三降噪系数。
需要说明的是，在本发明实施例中，由于当前帧图像的LSC特性曲线表示的是增益值与当前帧图像的不同位置距当前帧图像中心的距离，在本发明实施例中，终端将当前帧图像划分为N个区域后，以每个区域的中心点位置作为每个区域在当前帧图像中的位置，因此，终端就可以通过第一距离和LSC特性曲线表示的第一镜头均匀性补偿参数，确定出每个区域对应的每个第一图像对应的每个第一增益值了，该终端可以得到N个第一增益值，每个第一增益值与每个第一图像一一对应。
在本发明实施例中，终端将当前帧图像划分成的N个区域可以是矩形区域，也就是说，终端可以将当前帧图像划分为m×n＝N个矩形区域。
示例性的，终端将图像分成m*n个区域，计算每个区域的中心点与当前帧图像中心点的第一距离；并通过查表或者函数计算法在当前帧图像的LSC曲线中找到对应每个第一距离进行增加的每个第一增益值；如下：
(1)、终端将整个图像分成m*n个区域，具体区域表示如下：
Ration0,0、Ration0,1、Ration0,2、……、Rationm-1,n-1，m和n的值可以自行设定；以当前帧图像的图像中心为圆心，计算每个区域的中心位置距离该图像中心位置的第一距离，记录为：Dis.Ration0,0、Dis.Ration0,1、Dis.Ration0,2、……、Dis.Rationm-1,n-1；
(2)、终端根据第一距离，通过查表法在当前帧图像的LSC曲线中找到每个区域在LSC算法中增加的增益值：gain.Ration0,0、gain.Ration0,1、gain.Ration0,2、……、gain.Rationm-1,n-1。其中，查表法：首先将LSC曲线抽点采样，得到一个二维图表(距离和增益值的二维图表)，然后根据每个Dis.Region在图表中查找对应的gain.Region；当某个Dis.Region处于两个DIS中间位置时，采用线性插值得到该Dis对应的gain。
(3)、终端将增益值转换成降噪系数：ratio.Ration0,0、ratio.Ration0,1、ratio.Ration0,2、……、ratio.Rationm-1,n-1；首先以图像传感器的min gain和max gain为行坐标，根据不同gain对应的噪点强弱程度设定降噪等级，生成一个二维图表(gain和ratio的二维图表)，因为不同亮度环境下LSC可以设置不同的校正等级，所以二维图表可以分为bright light、normal light和lowlight三种情况或者更细分类。终端可以根据每个gain.Region在二维图表中查找对应的ratio.Region；当某个ratio.Region处于两个ratio中间位置时，终端采用线性插值得到该gain对应的ratio。这样终端就可以根据第一镜头均匀性补偿参数，确定当前帧图像的第一降噪系数了。
表1
Figure PCTCN2017085512-appb-000001
其中，表1为增益1倍时，距离和增益值的二维图表和gain和ratio的二维图表的组合图表。
S104、根据第一降噪系数对当前帧图像进行降噪处理。
终端确定当前帧图像的第一降噪系数之后，该终端就可以根据第一降噪系数对当前帧图像进行降噪处理了。
具体的，终端就可以根据N个第三降噪系数对N个第一图像分别进行降噪处理，其中，该N个第三降噪系数与该N个第一图像一一对应。
示例性的，终端可以使用获得的第一降噪系数来调整当前帧图像对应的各个区域的降噪等级了，即确定每个区域的第三降噪系数，降噪等级可以表示为：noise.Ration0,0、noise.Ration0,1、noise.Ration0,2……noise.Rationm-1,n-1。这样终端就根据降噪等级对应的降噪数据进行降噪处理了。
需要说明的是，在本发明实施例中，由于终端在获取当前帧图像的时候，可以根据上一帧图像的变化，调整对当前帧图像的降噪系数，从而实现对不同帧图像的差异化进行图像降噪处理，进而经图像降噪后提高了图像质量。
实施例二
本发明实施例提供了一种图像降噪方法，如图7所示，该方法可以包括：
S201、获取当前帧图像。
在本发明实施例中，“终端获取当前帧图像”的过程与前述实施例中的S101的 描述一致，此处不再赘述。
S202、获取与当前帧图像对应的第一镜头参数。
在本发明实施例中，“终端获取与当前帧图像对应的第一镜头参数”的过程与前述实施例中的S102的描述一致，此处不再赘述。
S203、获取上一帧图像对应的第二镜头均匀性特性参数。
S204、根据预设容忍阈值，判断第一镜头均匀性特性参数和第二镜头均匀性特性参数是否存在差异，其中，第一镜头参数包括：第一镜头均匀性特性参数和第一镜头均匀性补偿参数。
S205、若判断出存在差异，则根据第一镜头均匀性补偿参数，确定当前帧图像的第一降噪系数。
终端获取上一帧图像对应的第二镜头均匀性特性参数，并根据预设容忍阈值、第二镜头均匀性特性参数、第一镜头均匀性特性参数和第一镜头均匀性补偿参数，确定当前帧图像的第一降噪系数。
具体的，终端在获取与该当前帧图像对应的第一镜头均匀性特性参数和第一镜头均匀性补偿参数(即第一镜头参数)之后，由于该终端在实时的获取当前帧图像，并且对每帧图像的处理方式都是一样的，因此，在终端获取最新的当前帧图像时，该终端也可以通过同样的方式获取到上一帧图像对应的第二镜头均匀性特性参数。这样，终端就可以通过第一镜头均匀性特性参数、第二镜头均匀性特性参数和预设容忍阈值可以判断出上一帧图像与当前帧图像是否存在差异。然后，终端可以根据判断差异的结果，确定是通过第一镜头均匀性补偿参获取第一降噪系数对当前帧图像进行降噪，还是获取上一帧图像的降噪系数作为第一降噪系数对当前帧图像进行降噪。终端可以根据预设容忍阈值，判断第一镜头均匀性特性参数和第二镜头均匀性特性参数是否存在差异。若判断出存在差异，终端则根据第一镜头均匀性补偿参数，确定当前帧图像的第一降噪系数。
更具体的，在本发明实施例中，终端根据第一镜头均匀性补偿参数，确定当前帧图像的第一降噪系数的过程可以为：终端将当前帧图像划分为N个区域对应的N个第一图像，其中，N为大于1的自然数；终端根据第一镜头均匀性补偿参数，确定N个第一图像分别对应的N个第三降噪系数，将N个第三降噪系数确定为当前帧图像对应的第一降噪系数，其中，该N个第一图像与N个第三降噪系数一一对应。而终端根据第一镜头均匀性补偿参数，确定N个第一图像分别对应的N个第三降噪系数 的过程具体为：终端计算每个第一图像的中心点与当前帧图像的中心点的第一距离；然后，终端根据第一距离和第一镜头均匀性补偿参数，确定每个第一图像对应的每个第一增益值；最后，终端可以根据每个第一增益值转换成每个第一图像对应的第三降噪系数，直至得到N个第三降噪系数。
需要说明的是，在本发明实施例中，由于当前帧图像的LSC特性曲线表示的是增益值与当前帧图像的不同位置距当前帧图像中心的距离，在本发明实施例中，终端将当前帧图像划分为N个区域后，以每个区域的中心点位置作为每个区域在当前帧图像中的位置，因此，终端就可以通过第一距离和LSC特性曲线表示的第一镜头均匀性补偿参数，确定出每个区域对应的每个第一图像对应的每个第一增益值了，该终端可以得到N个第一增益值，每个第一增益值与每个第一图像一一对应。
在本发明实施例中，终端将当前帧图像划分成的N个区域可以是矩形区域，也就是说，终端可以将当前帧图像划分为m×n＝N个矩形区域。
示例性的，终端将图像分成m*n个区域，计算每个区域的中心点与当前帧图像中心点的第一距离；并通过查表或者函数计算法在当前帧图像的LSC曲线中找到对应每个第一距离进行增加的每个第一增益值；如下：
(1)、终端将整个图像分成m*n个区域，具体区域表示如下：
Ration0,0、Ration0,1、Ration0,2、……、Rationm-1,n-1，m和n的值可以自行设定；以当前帧图像的图像中心为圆心，计算每个区域的中心位置距离该图像中心位置的第一距离，记录为：Dis.Ration0,0、Dis.Ration0,1、Dis.Ration0,2、……、Dis.Rationm-1,n-1；
(2)、终端根据第一距离，通过查表法在当前帧图像的LSC曲线中找到每个区域在LSC算法中增加的增益值：gain.Ration0,0、gain.Ration0,1、gain.Ration0,2、……、gain.Rationm-1,n-1。其中，查表法：首先将LSC曲线抽点采样，得到一个二维图表(距离和增益值的二维图表)，然后根据每个Dis.Region在图表中查找对应的gain.Region；当某个Dis.Region处于两个DIS中间位置时，采用线性插值得到该Dis对应的gain。
(3)、终端将增益值转换成降噪系数：ratio.Ration0,0、ratio.Ration0,1、ratio.Ration0,2、……、ratio.Rationm-1,n-1；首先以图像传感器的min gain和max gain为行坐标，根据不同gain对应的噪点强弱程度设定降噪等级，生成一个二维图表(gain和ratio的二维图表)，因为不同亮度环境下LSC可以设置不同的校正等级，所以二维图表可以分为bright light、normal light和lowlight三种情况或者更细分类。终端可以根据每个gain.Region在二维图表中查找对应的ratio.Region；当某个ratio.Region 处于两个ratio中间位置时，终端采用线性插值得到该gain对应的ratio。这样终端就可以根据第一镜头均匀性补偿参数，确定当前帧图像的第一降噪系数了。
表1
Figure PCTCN2017085512-appb-000002
其中，表1为增益1倍时，距离和增益值的二维图表和gain和ratio的二维图表的组合图表。
进一步地，在本发明实施例中，由于终端根据第二镜头均匀性特性参数还可以获取到与上一帧图像对应的第二镜头均匀性补偿参数或是直接通过算法获取到第二镜头均匀性补偿参数，因此，终端还可以通过第一镜头均匀性补偿参数、第二镜头均匀性补偿参数和预设容忍阈值可以判断出上一帧图像与当前帧图像是否存在差异。本发明实施例不限制判断上一帧图像与当前帧图像是否存在差异的方式。
需要说明的是，在本发明实施例中，终端可以忽略上一帧图像与当前帧图像的微小亮度差异或区别，因此，设置预设容忍阈值来进行上一帧图像与当前帧图像的差异明显的情况。
可选的，在本发明实施例中，预设容忍阈值可以预先设好，也可以根据用户需求进行相应的调整，预设容忍阈值可经过实验结果得到，不宜设置的过大或过小，由于预设容忍阈值设置过大，则相邻两帧图像对应的特性曲线发生一定程度的变化时，不会实时确定新的降噪系数；如果预设容忍阈值设置过小，则相邻两帧数据之间微小的变化也会重新更新降噪系数，使得图像处理数据较大，耗时较长。
S206、若判断出未存在差异，则获取上一帧图像对应的第二降噪系数，将该第二降噪系数确定为第一降噪系数。
终端在获取与该当前帧图像对应的第一镜头参数之后，由于该终端在实时的获取 当前帧图像，并且对每帧图像的处理方式都是一样的，因此，在终端获取最新的当前帧图像时，该终端也可以通过同样的方式获取到上一帧图像对应的第二镜头均匀性特性参数。这样，终端就可以通过第一镜头均匀性特性参数、第二镜头均匀性特性参数和预设容忍阈值可以判断出上一帧图像与当前帧图像是否存在差异。然后，终端可以根据判断差异的结果，确定是通过第一镜头均匀性补偿参获取第一降噪系数对当前帧图像进行降噪，还是获取上一帧图像的降噪系数作为第一降噪系数对当前帧图像进行降噪。
具体的，终端可以根据预设容忍阈值，判断第一镜头均匀性特性参数和第二镜头均匀性特性参数是否存在差异。若判断出未存在差异，则表明终端目前使用的降噪系数，即上一帧图像的降噪系数是可以继续使用的，也就是说，终端可以获取上一帧图像对应的第二降噪系数，将该第二降噪系数确定为第一降噪系数。
S207、根据第一降噪系数对当前帧图像进行降噪处理。
终端确定了第一降噪系数之后，该终端就可以根据N个第三降噪系数对N个第一图像分别进行降噪处理，其中，该N个第三降噪系数与该N个第一图像一一对应。
示例性的，终端可以使用获得的第一降噪系数来调整当前帧图像对应的各个区域的降噪等级了，即确定每个区域的第三降噪系数，降噪等级可以表示为：noise.Ration0,0、noise.Ration0,1、noise.Ration0,2……noise.Rationm-1,n-1。这样终端就根据降噪等级对应的降噪数据进行降噪处理了。
需要说明的是，在本发明实施例中，由于终端在获取当前帧图像的时候，可以根据上一帧图像的变化，调整对当前帧图像的降噪系数，从而实现对不同帧图像的差异化进行图像降噪处理，进而经图像降噪后提高了图像质量。
实施例三
如图8所示，本发明实施例提供了一种终端1，该终端1可以包括：
获取单元10，用于获取当前帧图像；及获取与所述当前帧图像对应的第一镜头参数；
确定单元11，用于根据所述第一镜头参数，确定所述当前帧图像的第一降噪系数。
降噪单元12，用于根据所述第一降噪系数对所述当前帧图像进行降噪处理。
可选的，基于图8，如图9所示，所述终端1还包括：判断单元13。
所述获取单元，还用于获取上一帧图像对应的第二镜头均匀性特性参数。
所述判断单元13，用于根据预设容忍阈值，判断所述第一镜头均匀性特性参数和所述第二镜头均匀性特性参数是否存在差异，所述第一镜头参数包括：第一镜头均匀性特性参数和第一镜头均匀性补偿参数。
所述确定单元11，具体用于若判断出存在差异，或者，以及若判断出未存在差异，则获取所述上一帧图像对应的第二降噪系数，将所述第二降噪系数确定为所述第一降噪系数。
可选的，基于图9，如图10所示，所述终端1还包括：划分单元14。
所述划分单元14，用于将所述当前帧图像划分为N个区域对应的N个第一图像。
所述确定单元11，还具体用于根据所述第一镜头参数，确定所述N个第一图像分别对应的N个第三降噪系数，将所述N个第三降噪系数确定为所述当前帧图像对应的所述第一降噪系数，其中，所述N个第一图像与所述N个第三降噪系数一一对应。
可选的，基于图10，如图11所示，所述终端1还包括：计算单元15和转换单元16。
所述计算单元15，用于计算每个第一图像的中心点与所述当前帧图像的中心点的第一距离。
所述确定单元11，还具体用于根据所述第一距离和所述第一镜头参数，确定所述每个第一图像对应的每个第一增益值。
所述转换单元16，用于根据所述每个第一增益值转换成所述每个第一图像对应的第三降噪系数，直至得到所述N个第三降噪系数。
可选的，所述降噪单元12，具体用于根据所述N个第三降噪系数对所述N个第一图像分别进行降噪处理，其中，所述N个第三降噪系数与所述N个第一图像一一对应。
如图12所示，在实际应用中，上述获取单元10、确定单元11，降噪单元12、判断单元13、划分单元14、计算单元15和转换单元16可由位于第一终端上的处理器17实现，具体为中央处理器(CPU)、微处理器(MPU)、数字信号处理器(DSP)或现场可编程门阵列(FPGA)等实现，上述第一终端还包括：存储介质18实现，该存储介质18可以通过***总线19与处理器17连接，其中，存储介质18用于存储可执行程序代码，该程序代码包括计算机操作指令，存储介质18可能包含高速RAM存储器，也可能还包括非易失性存储器，例如，至少一个磁盘存储器。
需要说明的是，在本发明实施例中，由于终端在获取当前帧图像的时候，可以根据上一帧图像的变化，调整对当前帧图像的降噪系数，从而实现对不同帧图像的差异化进行图像降噪处理，进而经图像降噪后提高了图像质量。
本领域内的技术人员应明白，本发明的实施例可提供为方法、***、或计算机程序产品。因此，本发明的实施例可采用硬件实施例、软件实施例、或结合软件和硬件方面的实施例的形式。而且，本发明的实施例可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器和光学存储器等)上实施的计算机程序产品的形式。
本发明是参照根据本发明实施例的方法、设备(***)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
以上所述，仅为本发明的较佳实施例而已，并非用于限定本发明的保护范围。
工业实用性
本发明实施例提供的技术方案可以应用于图像处理领域。在本发明实施例提供的图像降噪方法及终端中，获取当前帧图像；获取与当前帧图像对应的第一镜头参数；根据第一镜头参数，确定当前帧图像的第一降噪系数；根据第一降噪系数对当前帧图像进行降噪处理。采用上述方案实现技术，由于终端可以对每帧图像基于镜头参数的 考虑进行不同降噪系数的选择，从而实现对不同帧图像选择合适的降噪参数进行降噪处理的差异化，进而通过实现差异化进行图像降噪处理，提高了图像质量。

Claims (11)

  1. 一种图像降噪方法，其中，包括：
    获取当前帧图像；
    获取与所述当前帧图像对应的第一镜头参数；
    根据所述第一镜头参数，确定所述当前帧图像的第一降噪系数；
    根据所述第一降噪系数对所述当前帧图像进行降噪处理。
  2. 根据权利要求1所述的方法，其中，所述根据所述第一镜头参数，确定所述当前帧图像的第一降噪系数，包括：
    将所述当前帧图像划分为N个区域对应的N个第一图像，其中，N为大于1的自然数；
    根据所述第一镜头参数，确定所述N个第一图像分别对应的N个第三降噪系数，将所述N个第三降噪系数确定为所述当前帧图像对应的所述第一降噪系数，其中，所述N个第一图像与所述N个第三降噪系数一一对应。
  3. 根据权利要求2所述的方法，其中，所述根据所述第一镜头均匀性特性参数和所述第一镜头参数，确定所述N个第一图像分别对应的N个第三降噪系数，包括：
    计算每个第一图像的中心点与所述当前帧图像的中心点的第一距离；
    根据所述第一距离和所述第一镜头参数，确定所述每个第一图像对应的每个第一增益值；
    根据所述每个第一增益值转换成所述每个第一图像对应的第三降噪系数，直至得到所述N个第三降噪系数。
  4. 根据权利要求2或3所述的方法，其中，所述根据所述第一降噪系数对所述当前帧图像进行降噪处理，包括：
    根据所述N个第三降噪系数对所述N个第一图像分别进行降噪处理，其中，所述N个第三降噪系数与所述N个第一图像一一对应。
  5. 根据权利要求1所述的方法，其中，所述根据所述第一镜头参数，确定所述当前帧图像的第一降噪系数，包括：
    获取上一帧图像对应的第二镜头均匀性特性参数；
    根据预设容忍阈值，判断第一镜头均匀性特性参数和所述第二镜头均匀性特性参数是否存在差异；所述第一镜头参数包括：第一镜头均匀性特性参数和第一镜头 均匀性补偿参数；
    若判断出存在差异，则根据所述第一镜头均匀性补偿参数，确定所述当前帧图像的第一降噪系数；
    若判断出未存在差异，则获取所述上一帧图像对应的第二降噪系数，将所述第二降噪系数确定为所述第一降噪系数。
  6. 一种终端，其中，包括：
    获取单元，设置为获取当前帧图像；及获取与所述当前帧图像对应的第一镜头参数；
    确定单元，设置为根据所述第一镜头参数，确定所述当前帧图像的第一降噪系数；
    降噪单元，设置为根据所述第一降噪系数对所述当前帧图像进行降噪处理。
  7. 根据权利要求6所述的终端，其中，所述终端还包括：划分单元；
    所述划分单元，设置为将所述当前帧图像划分为N个区域对应的N个第一图像，其中，N为大于1的自然数；
    所述确定单元，设置为根据所述第一镜头参数，确定所述N个第一图像分别对应的N个第三降噪系数，将所述N个第三降噪系数确定为所述当前帧图像对应的所述第一降噪系数，其中，所述N个第一图像与所述N个第三降噪系数一一对应。
  8. 根据权利要求7所述的终端，其中，所述终端还包括：计算单元和转换单元；
    所述计算单元，设置为计算每个第一图像的中心点与所述当前帧图像的中心点的第一距离；
    所述确定单元，还设置为根据所述第一距离和所述第一镜头参数，确定所述每个第一图像对应的每个第一增益值；
    所述转换单元，设置为根据所述每个第一增益值转换成所述每个第一图像对应的第三降噪系数，直至得到所述N个第三降噪系数。
  9. 根据权利要求7或8所述的终端，其中，
    所述降噪单元，设置为根据所述N个第三降噪系数对所述N个第一图像分别进行降噪处理，其中，所述N个第三降噪系数与所述N个第一图像一一对应。
  10. 根据权利要求6所述的终端，其中，所述终端还包括：判断单元；
    所述获取单元，还设置为获取上一帧图像对应的第二镜头均匀性特性参数；
    所述判断单元，设置为根据预设容忍阈值，判断所述第一镜头均匀性特性参数 和所述第二镜头均匀性特性参数是否存在差异；所述第一镜头参数包括：第一镜头均匀性特性参数和第一镜头均匀性补偿参数；
    所述确定单元，设置为若判断出存在差异，则根据所述第一镜头均匀性补偿参数，确定所述当前帧图像的第一降噪系数；或者，若判断出未存在差异，则获取所述上一帧图像对应的第二降噪系数，将所述第二降噪系数确定为所述第一降噪系数。
  11. 一种计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令用于执行如权利要求1至5中所述的方法中的至少其中之一。
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