CN109636743B - Method and device for removing image noise - Google Patents
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Abstract
The invention discloses a method for removing image noise, which comprises the following steps: s01: acquiring an image to be processed, and decomposing the image on an RGB color gamut; s02: respectively calculating first-order differential characteristic functions g (x, y) of different channel images; s03: acquiring a binary image function B (x, y) of the first-order differential characteristic function g (x, y) in each channel image; s04: marking a binary image function B (x, y) region in each channel image by adopting a connected region marking algorithm to form a connected region; s05: marking a communication region with the area smaller than the threshold value of the corresponding region of the channel in each channel image as a noise region, and marking a communication region with the area larger than the threshold value of the corresponding region of the channel as an edge region; s06: and respectively processing the noise area and the edge area in each channel image. According to the method and the device for removing the image noise, the noise area and the edge area are identified according to the communication characteristics of the noise and the edge, and the edge area can be protected while the noise is restrained.
Description
Technical Field
The invention relates to the field of data identification, in particular to a method and a device for removing image noise.
Background
Since various electrical noises exist in the Image process of the Image Sensor (CIS, CMOS Image Sensor), various noise points are represented on the Image, and the properties of the noise points are modeled by various statistical models in the traditional process, but in the actual Image processing process, the noise area and the edge area often need to be compromised, that is, the edge area is smoothed while noise is suppressed.
The existing model and processing method for distinguishing the edge area and the noise area can damage the edge area when processing noise, so that the edge area of the whole image is incomplete, and the quality of the whole image is affected.
Disclosure of Invention
The invention aims to provide a method and a device for removing image noise, which are used for identifying a noise area and an edge area according to the communication characteristics of noise and edges, so that the edge area can be protected while the noise area is restrained.
In order to achieve the above purpose, the present invention adopts the following technical scheme: a method of removing image noise, comprising the steps of:
S01: acquiring an image to be processed, and decomposing the image on an RGB color gamut to form an R channel image, a G channel image and a B channel image;
S02: respectively calculating first-order differential characteristic functions g (x, y) of different channel images;
s03: acquiring a binary image function B (x, y) corresponding to the first-order difference characteristic function g (x, y) in each channel image; the binary map function B (x, y) =g (x, y) > thr; the thr represents a differential threshold;
s04: marking the corresponding areas of the binary image functions B (x, y) in each channel image by adopting a connected area marking algorithm to form connected areas, and carrying out area statistics on each connected area;
S05: marking a communication region with the area smaller than the threshold value of the corresponding region of the channel in each channel image as a noise region, and marking a communication region with the area larger than the threshold value of the corresponding region of the channel as an edge region;
S06: and carrying out smoothing treatment on the noise area in each channel image, carrying out sharpening treatment on the edge area, and synthesizing each processed channel image into a new image, namely the image after noise removal.
Further, the first order differential characteristic function g (x, y) = iil f (x-1, y) -f (x+1, y) |iif (x, y-1) -f (x, y+1) |; where f (x, y) represents the original image, || is a norm or gradient operator.
Further, the connected region marking algorithm is 8 or 4 connected.
Further, the area statistics of each connected region includes statistics of total pixel of the connected region.
Further, the step S02 includes: calculating a first-order differential characteristic function g 1 (x, y) of the image R channel image; calculating a first-order differential characteristic function g 2 (x, y) of the image B channel image; and calculating a first-order differential characteristic function G 3 (x, y) of the image G channel image.
Further, the step S03 includes: calculating a binary image function B 1 (x, y) of the R channel image; calculating a binary image function B 2 (x, y) of the B channel image; a binary map function B 3 (x, y) of the G-channel image is calculated.
Further, respectively marking the areas corresponding to the binary image functions in the R channel image, the B channel image and the G channel image as m, and marking the areas marked as m in different channel images by adopting a connected area marking algorithm to form x connected areas, wherein x is an integer greater than or equal to 1;
further, smoothing filtering method is adopted to carry out smoothing processing on the noise area.
The invention provides a device for removing image noise, which comprises a first-order difference generation module, a first-order difference judgment module, a communication area marking module, a communication area statistics module, a judgment module, a noise processing module and an edge processing module; the method comprises the steps that an image to be processed is input into a first-order difference generating module to form a first-order difference characteristic function g (x, y) of a sub-channel image and is transmitted to the first-order difference judging module, the first-order difference judging module generates a binary image function B (x, y) of the first-order difference characteristic function g (x, y) and is transmitted to a communicating region marking module, the communicating region marking module marks according to the binary image function B (x, y) to form communicating regions of each channel image and is transmitted to a communicating region area counting module, the communicating region area counting module counts the area of each communicating region and is transmitted to a judging module, the judging module judges whether each communicating region is a noise region or an edge region according to a region threshold corresponding to each channel, the judged noise region is transmitted to a noise processing module for processing, the judged edge region is transmitted to the edge processing module for processing, and each channel image after processing is synthesized into an image, namely the image after noise removal.
The beneficial effects of the invention are as follows: the invention adopts the connected region statistics, can effectively judge whether the abrupt change region is a noise region or an edge region, can effectively protect the image edge region when the noise region is processed, can further carry out different processing on the noise region and the edge region, and simultaneously processes the correction of the noise region and the edge region; by adopting the method for processing noise by the multichannel images, the noise of each channel image can be efficiently and rapidly removed, and the overall quality of the image is further improved.
Drawings
Fig. 1 is a flowchart of a method for removing image noise according to the present invention.
Fig. 2 is a schematic structural diagram of an apparatus for removing image noise according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following detailed description of the specific embodiments of the present invention will be given with reference to the accompanying drawings.
The greatest difference between noise and edges is that noise is isolated and edges are widely connected. From an image statistics perspective, the edge span is typically a banded region of multiple pixels. Therefore, the first-order differential characteristics of the image can be calculated through various operators, the distribution of the edge area and the noise area is obtained, and then the area of the connected area in the first-order differential graph is counted. When the area is smaller than the specific pixel value, determining the area as a noise area, and then filtering noise; when the area is larger than the specific pixel value, it is determined as an edge area, and the edge area is reinforced.
As shown in fig. 1, the method for removing image noise provided by the invention comprises the following steps:
s01: an image to be processed is acquired.
S02: the multichannel image calculates a first order differential characteristic function g (x, y) of the image. First order differential characteristic function g (x, y) = l f (x-1, y) -f (x+1, y) [ l+ [ l f (x, y-1) -f (x, y+1) [ l; where f (x, y) represents the original image, || is a norm or other gradient operator.
The method specifically comprises the following steps: calculating a first-order differential characteristic function g 1 (x, y) of the image R channel image; calculating a first-order differential characteristic function g 2 (x, y) of the image B channel image; the first order differential characteristic function G 3 (x, y) of the image G channel image is calculated.
S03: acquiring a binary image function B (x, y) corresponding to a first-order differential characteristic function g (x, y) in each channel image; binary map function B (x, y) =g (x, y) > thr; thr represents a differential threshold.
The method specifically comprises the following steps: calculating a binary image function B 1 (x, y) of the R channel image; calculating a binary image function B 2 (x, y) of the B channel image; a binary map function B 3 (x, y) of the G-channel image is calculated.
S04: and marking the binary image function B (x, y) areas in each channel image by adopting a connected area marking algorithm to form connected areas, and carrying out area statistics on each connected area. The connected region marking algorithm is 8 or 4 connected. The area statistics of each connected region includes statistics of the total amount of pixels of the connected region.
The method specifically comprises the following steps: and respectively marking the areas corresponding to the binary image functions in the R channel image, the B channel image and the G channel image as m, and marking the areas marked as m in different channel images by adopting a connected area marking algorithm to form x connected areas, wherein x is an integer greater than or equal to 1, and m can be marked as numbers or letters and the like as long as the effect of distinguishing the marks can be achieved.
In this step, three channel images may be marked simultaneously, or they may be marked separately, and different marking symbols may be set. Because the function values of the binary image corresponding to each channel image are the same, the denoising process can be accelerated by adopting the same marking method.
S05: and marking a communication region with the area smaller than the threshold value of the corresponding region of the channel in each channel image as a noise region, and marking a communication region with the area larger than the threshold value of the corresponding region of the channel as an edge region. The area threshold corresponding to the channel refers to an area threshold corresponding to three channels, and the area thresholds in the three channels may be the same value or different values. On the other hand, the area threshold is a threshold set in advance, and the magnitude of the threshold is related to the degree of noise removal, and if the area threshold is set to be smaller in an image having a strict noise interference requirement, most of noise can be removed, and the threshold is set specifically according to the noise removal requirement.
S06: and carrying out smoothing treatment on noise areas in the channel images, carrying out sharpening treatment on the edge areas, and synthesizing the treated channel images into a new image, namely the image after noise removal. Wherein, a smoothing filtering method can be adopted to carry out smoothing processing on the noise area.
As shown in figure 2, the device for removing image noise provided by the invention comprises a first-order difference generation module, a first-order difference judgment module, a communication area marking module, a communication area statistics module, a judgment module, a noise processing module and an edge processing module; the method comprises the steps that an image to be processed is input into a first-order difference generating module to form a first-order difference characteristic function g (x, y) of a sub-channel image, the first-order difference characteristic function g (x, y) is transmitted to a first-order difference judging module, the first-order difference judging module generates a binary image function B (x, y) of the first-order difference characteristic function g (x, y) and transmits the binary image function B (x, y) to a connected region marking module, the connected region marking module marks according to the binary image function B (x, y) to form connected regions of each channel image, the connected region area counting module counts the area of each connected region, the counting result is transmitted to a judging module, the judging module judges whether each connected region is a noise region or an edge region according to a region threshold corresponding to each channel, the judged noise region is transmitted to a noise processing module for processing, the judged edge region is transmitted to an edge processing module for processing, and each channel image after processing is synthesized into an image, namely the image after noise is removed.
The invention adopts the connected region statistics, can effectively judge whether the abrupt change region is a noise region or an edge region, can effectively protect the image edge region when noise is processed, can further carry out different processing on the noise region and the edge region, and simultaneously processes the correction of the noise region and the edge region; by adopting the method for processing noise by the multichannel images, the noise of each channel image can be efficiently and rapidly removed, and the overall quality of the image is further improved.
The foregoing description is only of the preferred embodiments of the present invention, and the embodiments are not intended to limit the scope of the invention, so that all changes made in the structure and details of the invention which may be regarded as equivalents thereof are intended to be included within the scope of the invention as defined in the following claims.
Claims (4)
1. A method of removing image noise, comprising the steps of:
S01: acquiring an image to be processed, and decomposing the image on an RGB color gamut to form an R channel image, a G channel image and a B channel image;
S02: respectively calculating first-order differential characteristic functions g (x, y) of different channel images; the first-order differential characteristic function g (x, y) = ||f (x-1, y) -f (x+1, y) |+|f (x, y-1) -f (x, y+1) |; where f (x, y) represents the original image, the norm or gradient operator; calculating a first-order differential characteristic function g 1 (x, y) of the image R channel image; calculating a first-order differential characteristic function g 2 (x, y) of the image B channel image; calculating a first-order differential characteristic function G 3 (x, y) of the image G channel image;
S03: acquiring a binary image function B (x, y) corresponding to the first-order difference characteristic function g (x, y) in each channel image; the binary map function B (x, y) =g (x, y) > thr; the thr represents a differential threshold; calculating a binary image function B 1 (x, y) of the R channel image; calculating a binary image function B 2 (x, y) of the B channel image; calculating a binary image function B 3 (x, y) of the G channel image;
S04: marking the corresponding areas of the binary image functions B (x, y) in each channel image by adopting a connected area marking algorithm to form connected areas, and carrying out area statistics on each connected area; the communication area marking algorithm is 8 communication or 4 communication; the area statistics of each connected region comprises the statistics of the total pixel quantity of the connected region;
S05: marking a communication region with the area smaller than the threshold value of the corresponding region of the channel in each channel image as a noise region, and marking a communication region with the area larger than the threshold value of the corresponding region of the channel as an edge region;
S06: and carrying out smoothing treatment on the noise area in each channel image, carrying out sharpening treatment on the edge area, and synthesizing each processed channel image into a new image, namely the image after noise removal.
2. The method of removing image noise according to claim 1, wherein the step S04 includes: and respectively marking the areas corresponding to the binary image functions in the R channel image, the B channel image and the G channel image as m, and marking the areas marked as m in different channel images by adopting a connected area marking algorithm to form x connected areas, wherein x is an integer greater than or equal to 1.
3. A method of removing image noise according to claim 1, wherein the noise region is smoothed by a smoothing filter method.
4. The device for removing image noise is characterized by comprising a first-order difference generation module, a first-order difference judgment module, a connected region marking module, a connected region area statistics module, a judgment module, a noise processing module and an edge processing module, wherein the first-order difference judgment module is used for judging whether the connected region marking module is connected with the connected region area statistics module; the method comprises the steps that an image to be processed is input into a first-order difference generating module to form a first-order difference characteristic function g (x, y) of a sub-channel image and is transmitted to the first-order difference judging module, the first-order difference judging module generates a binary image function B (x, y) of the first-order difference characteristic function g (x, y) and is transmitted to a communicating region marking module, the communicating region marking module marks according to the binary image function B (x, y) to form communicating regions of each channel image and is transmitted to a communicating region area counting module, the communicating region area counting module counts the area of each communicating region and is transmitted to a judging module, the judging module judges whether each communicating region is a noise region or an edge region according to a region threshold corresponding to each channel, the judged noise region is transmitted to a noise processing module for processing, the judged edge region is transmitted to the edge processing module for processing, and each channel image after processing is synthesized into an image, namely the image after noise removal.
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| CN101901477A (en) * | 2010-07-27 | 2010-12-01 | 中国农业大学 | Method and system for extracting field image edges of plant leaves |
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| CN108109123A (en) * | 2017-12-21 | 2018-06-01 | 成都微光集电科技有限公司 | A kind of image de-noising method |
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