CN104021533A - Real-time image denoising method and device - Google Patents
Real-time image denoising method and device Download PDFInfo
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Abstract
The invention discloses a real-time image denoising method and device, which are used for denoising an image acquired by image acquiring equipment. The real-time image denoising method comprises the steps: for each pixel point of an input image, searching a corresponding noise threshold value from an image noise enveloping curve corresponding to a current gain value of the image acquiring equipment; calculating a difference value between an adjacent pixel point and the pixel point in each pixel point neighbor hood, making the adjacent pixel point that the absolute value of the difference value is less than the noise threshold value precipitate in denoising, otherwise, making the adjacent pixel point not participate in denoising; and for each pixel point, performing denoising by using the adjacent pixel point participating in the denoising in the neighbor hood. Meanwhile, the invention further discloses a device for realizing the method. The device comprises a look-up module, a detection module and a denoising module, and further comprises an image noise enveloping curve calibrating module. The method and device disclosed by the invention have the advantages of being high in processing speed, small in hardware realizing resource overhead, convenient to realize, strong in denoising pertinence, and good in effect.
Description
Technical field
The invention belongs to technical field of image processing, especially design a kind of method and device at image acquisition end realtime graphic noise reduction.
Background technology
Image usually declines to some extent picture quality because being subject to the interference of each noise like in the processes such as collection, transmission, storage, thereby follow-up image is processed and had a negative impact, and therefore in image processing field, image noise reduction is a very important research topic.Image noise reduction is to remove noise contribution from existing noisy image, improves image imaging quality.Image capture device is as web camera (IPC), digital camera etc., and after target object is taken, the RAW data of being caught by imageing sensor through colour filter array (CFA), need to carry out image noise reduction.
General pattern noise reduction can be divided into 2D noise reduction and 3D noise reduction, and 2D noise reduction only carries out noise reduction process on two-dimensional space territory; And 3D noise reduction has increased time domain processing, therefore become three-dimensional.The basic skills of 2D noise reduction is to a pixel, itself and surrounding pixel is average, average rear reducing noise, and its shortcoming is to cause fuzzy pictures, particularly object edge part.Therefore to the improvement of this noise-reduction method, be mainly to carry out rim detection, the pixel of marginal portion is not used for carrying out noise reduction.The difference of 3D noise reduction and 2D noise reduction is, 2D noise reduction is only considered a two field picture, and 3D noise reduction is further considered the time domain relation between frame and frame, and each pixel is carried out to average in time domain, by the change reducing in time domain, reduces noise.Compare 2D noise reduction, 3D noise reduction is better, and can not cause the fuzzy of edge, but the subject matter existing is: picture can not be completely static, if carry out noise reduction process and can make the mistake not belonging to two points of same object.Therefore the method needs estimation, and its effect quality is also relevant to estimation.And estimation calculated amount is large, length consuming time, is the Main Bottleneck of restriction 3D noise reduction.
The method of 2D noise reduction is a lot, and most basic method is medium filtering, the mean filter based on rim detection, although these class methods realize simply, is difficult to balance " reservation of marginal information " and " removal of noise ".Relatively customary way also has gaussian filtering, bilateral filtering, but these class methods realize more complicated conventionally, and it is large that hardware is realized resource overhead.
Because 2D noise reduction implements relatively easily, therefore how 2D noise reduction is improved, remain a direction of current research.
Summary of the invention
The object of this invention is to provide a kind of realtime graphic noise-reduction method and device, be used for the image of image capture device collection to carry out noise reduction, can avoid noise-reduction method of the prior art to realize more complicated, hardware is realized the problem that resource overhead is large, and noise reduction is with strong points, realize real-time noise-reducing.
To achieve these goals, technical solution of the present invention is as follows:
A realtime graphic noise-reduction method, for the image of image capture device collection is carried out to noise reduction, the method comprises the steps:
For each pixel of input picture, in picture noise enveloping curve corresponding to image capture device current gain value, find corresponding noise threshold value;
Calculate the difference of the interior neighbor pixel of each neighborhood of pixel points and this pixel, the neighbor pixel that the absolute value of difference is less than noise threshold value participates in noise reduction, otherwise does not participate in noise reduction;
To each pixel, utilize the neighbor pixel that participates in noise reduction in its neighborhood to carry out noise reduction process.
Further, the preparation method of described picture noise enveloping curve comprises step:
The poor δ of noise criteria that uncalibrated image collecting device different gains is corresponding;
According to the noise reduction intensity of setting, the picture noise enveloping curve according to following Formula corresponding to input picture: NoiseThr (y, X)=max ((f
-1(f (y)+X)-y), (y-f
-1(f (y)-X)));
Wherein y is the pixel value of pixel in input picture, X is the noise intensity of pixel, X=K* δ, K is noise reduction intensity, NoiseThr (y, X) is the maximum noise that pixel is corresponding, and f (y) is mapped to the gamma mapping function that display end shows for input picture, f (y)=y^ (1/r), r is gamma coefficient.
Further, for each pixel of input picture, its corresponding noise threshold value is exactly on described picture noise enveloping curve, to maximum noise NoiseThr (y, X) that should pixel pixel value, the pixel value that y is this pixel.
A kind of implementation of the present invention, describedly utilizes the neighbor pixel that participates in noise reduction in its neighborhood to carry out noise reduction process to each pixel, comprises step:
For each pixel, calculate and participate in the neighbor pixel of noise reduction and the mean value of its difference;
This mean value is added on this pixel, as the pixel value after this pixel noise reduction.
Another kind of implementation of the present invention, describedly utilizes the neighbor pixel that participates in noise reduction in its neighborhood to carry out noise reduction process to each pixel, comprises step:
For each pixel, for participating in the neighbor pixel of noise reduction, distribute noise reduction weight, and calculate the long-pending of this noise reduction weight and described difference;
The mean value of calculated product, is added to this mean value on this pixel, as the pixel value after this pixel noise reduction.
The invention allows for a kind of realtime graphic denoising device, for the image of image capture device collection is carried out to noise reduction, this device comprises:
Table look-up module for each pixel for input picture, finds corresponding noise threshold value in picture noise enveloping curve corresponding to image capture device current gain value;
Detection module, for calculating the difference of neighbor pixel and this pixel in each neighborhood of pixel points, the neighbor pixel that the absolute value of difference is less than noise threshold value participates in noise reduction, otherwise does not participate in noise reduction;
Noise reduction module, for utilizing the neighbor pixel that participates in noise reduction in its neighborhood to carry out noise reduction process to each pixel.
Further, described device also comprises picture noise enveloping curve demarcating module, described picture noise enveloping curve demarcating module is for the picture noise enveloping curve of uncalibrated image collecting device, and search module described in the picture noise enveloping curve of demarcation is input to, described picture noise enveloping curve demarcating module is carried out following steps when the picture noise enveloping curve of uncalibrated image collecting device:
The poor δ of noise criteria that uncalibrated image collecting device different gains is corresponding;
According to the noise reduction intensity of setting, the picture noise enveloping curve according to following Formula corresponding to input picture: NoiseThr (y, X)=max ((f
-1(f (y)+X)-y), (y-f
-1(f (y)-X)));
Wherein y is the pixel value of pixel in input picture, X is the noise intensity of pixel, X=K* δ, K is noise reduction intensity, NoiseThr (y, X) is the maximum noise that pixel is corresponding, and f (y) is mapped to the gamma mapping function that display end shows for input picture, f (y)=y^ (1/r), r is gamma coefficient.
Further, for each pixel of input picture, its corresponding noise threshold value is exactly on described picture noise enveloping curve, to maximum noise NoiseThr (y, X) that should pixel pixel value, the pixel value that y is this pixel.
A kind of implementation of the present invention, described noise reduction module, when carrying out image noise reduction processing, is carried out following steps:
For each pixel, calculate and participate in the neighbor pixel of noise reduction and the mean value of its difference;
This mean value is added on this pixel, as the pixel value after this pixel noise reduction.
Another kind of implementation of the present invention, described noise reduction module, when carrying out image noise reduction processing, is carried out following steps:
For each pixel, for participating in the neighbor pixel of noise reduction, distribute noise reduction weight, and calculate the long-pending of this noise reduction weight and described difference;
The mean value of calculated product, is added to this mean value on this pixel, as the pixel value after this pixel noise reduction.
The present invention proposes a kind of realtime graphic noise-reduction method and device, by demarcating the picture noise enveloping curve of collection terminal, and the maximum noise of usining on this curve corresponding to each pixel is as noise reduction threshold value.In noise reduction process, directly by the method for tabling look-up, obtaining this noise reduction threshold value carries out image noise reduction, do not need to utilize noise to estimate to go to judge flat region and marginarium, processing speed is fast, and it is little that hardware is realized resource overhead, it is convenient to realize, and the method excellent noise reduction effect.
Accompanying drawing explanation
Fig. 1 is that image acquisition end is to the mapping relations figure of display end;
Fig. 2 is collection terminal picture noise enveloping curve schematic diagram;
Fig. 3 is a kind of realtime graphic noise-reduction method of the present invention process flow diagram;
Fig. 4 is neighborhood of pixel points schematic diagram of the present invention;
Fig. 5 is a kind of realtime graphic denoising device of the present invention structural representation.
Embodiment
Below in conjunction with drawings and Examples, technical solution of the present invention is described in further details, following examples do not form limitation of the invention.
The mapping relations of image from collection terminal to display end general satisfaction Fig. 1, in Fig. 1, y is the pixel value of pixel in collection terminal image, f (y) is display end image, has passed through gamma mapping curve f (y)=y^ (1/r) from collection terminal to display end, and wherein r is gamma coefficient.
For the power of picture noise, by various noise estimation methods, can obtain the standard deviation of noise, and by standard deviation, carry out the power of presentation video noise.The theoretical foundation of doing is like this that the variance of image flat site should be minimum, and its variance should be mainly noise variance decision, noise variance evolution is just obtained to the standard deviation of noise.Tradition noise-reduction method directly takes standard deviation and noise judgement threshold value to make comparisons, if standard deviation is less than threshold value, explanation is flat region, can increase the weight of noise reduction, if standard deviation is greater than threshold value, explanation is marginarium, should suitably weaken noise reduction weight.
If the standard deviation of noise represents with δ, the present embodiment is multiplied by δ after a COEFFICIENT K, obtains X=K* δ, by the noise size of X presentation video.Under ideal state, image displayed value is f (y), and f (y) represents the not image of Noise, introduces on this basis after noise " ± X ", and display end image fluctuates between f (y)-X and f (y)+X.The noisy image of display end fluctuates between f (y)-X and f (y)+X, and establishing the anti-mapping function of gamma is f
-1(y), the reflection of demonstration image is mapped to after image acquisition end, the noisy image of collection terminal is at f
-1(f (y)-X) and f
-1fluctuation between (f (y)+X).
The noise that can further derive collection terminal image is at (f
-1(f (y)+X)-y) and (y-f
-1(f (y)-X)) between fluctuation, definition collection terminal picture noise enveloping curve is:
NoiseThr(y,X)＝max((f
-1(f(y)+X)-y),(y-f
-1(f(y)-X)))。
As shown in Figure 2, NoiseThr (y, X) is actually the maximum noise corresponding to pixel in input picture (pixel value is y), if take NoiseThr (y, X) as noise reduction threshold value, and effective detection noise.
The present embodiment just be take NoiseThr (y, X) as noise reduction threshold value, realizes image noise reduction and processes, and as shown in Figure 3, a kind of realtime graphic noise-reduction method of the present embodiment comprises the steps:
Step 301, for each pixel of input picture, in picture noise enveloping curve corresponding to image capture device current gain value, find corresponding noise threshold value.
For picture noise enveloping curve corresponding to image capture device current gain value, can complete demarcation in laboratory, specifically comprise step:
1), noise criteria corresponding to uncalibrated image collecting device different gains is poor.
For the image capture device of collection terminal, as video camera etc., inevitably there is noise in the RAW view data that its imageing sensor is caught.For image capture device, the poor δ of its corresponding noise criteria is closely related with the yield value of camera setting, can complete the demarcation of δ in laboratory.
For example, between the gain region of image capture device work, extract a typical n yield value, calibrate the poor δ of noise criteria that each yield value is corresponding.Wherein, for any one yield value, change illumination (corresponding to the pixel value of pixel) y obtains image, then by noise, estimates to obtain the variation relation that corresponding δ calibrates δ and illumination y.
2), according to the noise reduction intensity of setting, set up the picture noise enveloping curve corresponding to input picture.
The noise reduction intensity K arranging according to user, obtains picture noise X=K* δ, sets up the collection terminal picture noise enveloping curve NoiseThr (y, X) that input picture is corresponding.Under certain noise intensity estimated value X prerequisite, when collection terminal image pixel value is y, the maximum noise of image is NoiseThr (y, X), and this value can be used as noise reduction threshold value.
Thereby the image for input, can find corresponding picture noise enveloping curve according to image capture device current gain value, then according to the pixel value y of each pixel, finds corresponding noise threshold value.
Step 302, calculate the difference of neighbor pixel and this pixel in each neighborhood of pixel points, the absolute value of difference is less than the neighbor pixel participation noise reduction of noise threshold value, otherwise does not participate in noise reduction.
As shown in Figure 4, for any pixel P (i, j), (i, j) is coordinate, and centered by pixel P (i, j), the neighborhood of definition pixel P (i, j), as noise reduction template, is generally 3 * 3 or 5 * 5 or 7 * 7 square.Noise reduction template is pixel centered by pixel P (i, j), and other pixels are called its neighbor pixel.Calculate neighbor pixel in this neighborhood and the difference of central pixel point P (i, j), if the difference obtaining is less than noise threshold value, corresponding neighbor pixel participates in the noise reduction of central pixel point P (i, j), otherwise does not participate in noise reduction.
The difference is here the difference of pixel pixel value, and this pixel value is brightness value, i.e. its illumination y.
Step 303, to each pixel, utilize the neighbor pixel that participates in noise reduction in its neighborhood to carry out noise reduction process.
Particularly, calculate the mean value that participates in the pixel of noise reduction and the difference of central pixel point P (i, j), this mean value is added to central pixel point P (i, j) upper, reach the object of noise reduction.
It should be noted that, concrete denoise processing method does not limit the mean value of difference is added on pixel, also have other denoise processing methods, as participated in the pixel of noise reduction for each, distribute noise reduction weight, and then try to achieve the long-pending of difference and its noise reduction weight, the mean value of calculated product, by the mean value of the calculating pixel P (i that is added to, j) above, reach the object of noise reduction.
In sum, owing to having demarcated the poor δ of noise criteria of image capture device, and generated corresponding picture noise enveloping curve, can find out noise threshold value by image input value, and carry out noise reduction process accordingly.It should be noted that, for the different yield value of image capture device, there is the different poor δ of corresponding noise criteria, and corresponding picture noise enveloping curve, after input picture, can find corresponding picture noise enveloping curve according to the current yield value of image capture device, and further obtain noise reduction threshold value, the mode that employing is tabled look-up, has reduced the calculated amount in noise reduction process, and hardware is realized simple.
Fig. 5 shows a kind of realtime graphic denoising device of embodiment of the present invention structural representation, comprising:
Table look-up module for each pixel for input picture, finds corresponding noise threshold value in picture noise enveloping curve corresponding to image capture device current gain value;
Detection module, for calculating the difference of neighbor pixel and this pixel in each neighborhood of pixel points, the neighbor pixel that the absolute value of difference is less than noise threshold value participates in noise reduction, otherwise does not participate in noise reduction;
Noise reduction module, for utilizing the neighbor pixel that participates in noise reduction in its neighborhood to carry out noise reduction process to each pixel.
Further, this device also comprises picture noise enveloping curve demarcating module, picture noise enveloping curve demarcating module is for the picture noise enveloping curve of uncalibrated image collecting device, and search module described in the picture noise enveloping curve of demarcation is input to, described picture noise enveloping curve demarcating module is carried out following steps when the picture noise enveloping curve of uncalibrated image collecting device:
The poor δ of noise criteria that uncalibrated image collecting device different gains is corresponding;
According to the noise reduction intensity of setting, the picture noise enveloping curve according to following Formula corresponding to input picture: NoiseThr (y, X)=max ((f
-1(f (y)+X)-y), (y-f
-1(f (y)-X)));
Wherein y is the pixel value of pixel in input picture, X is the noise intensity of pixel, X=K* δ, K is noise reduction intensity, NoiseThr (y, X) is the maximum noise that pixel is corresponding, and f (y) is mapped to the gamma mapping function that display end shows for input picture, f (y)=y^ (1/r), r is gamma coefficient.
For each pixel of input picture, its corresponding noise threshold value is exactly on described picture noise enveloping curve, to maximum noise NoiseThr (y, X) that should pixel pixel value, the pixel value that y is this pixel.
In the present embodiment, noise reduction module, when carrying out image noise reduction processing, is carried out following steps:
For each pixel, calculate and participate in the neighbor pixel of noise reduction and the mean value of its difference;
This mean value is added on this pixel, as the pixel value after this pixel noise reduction.
The another kind of implementation of noise reduction module when carrying out image noise reduction processing comprises step:
For each pixel, for participating in the neighbor pixel of noise reduction, distribute noise reduction weight, and calculate the long-pending of this noise reduction weight and described difference;
The mean value of calculated product, is added to this mean value on this pixel, as the pixel value after this pixel noise reduction.
It should be noted that, this device is FPGA, and picture noise enveloping curve demarcating module, after having demarcated picture noise enveloping curve, is issued to searching in module of FPGA in the mode of list item.
Above embodiment is only in order to technical scheme of the present invention to be described but not be limited; in the situation that not deviating from spirit of the present invention and essence thereof; those of ordinary skill in the art are when making according to the present invention various corresponding changes and distortion, but these corresponding changes and distortion all should belong to the protection domain of the appended claim of the present invention.
Claims (10)
1. a realtime graphic noise-reduction method, for the image of image capture device collection is carried out to noise reduction, is characterized in that, the method comprises the steps:
For each pixel of input picture, in picture noise enveloping curve corresponding to image capture device current gain value, find corresponding noise threshold value;
Calculate the difference of the interior neighbor pixel of each neighborhood of pixel points and this pixel, the neighbor pixel that the absolute value of difference is less than noise threshold value participates in noise reduction, otherwise does not participate in noise reduction;
To each pixel, utilize the neighbor pixel that participates in noise reduction in its neighborhood to carry out noise reduction process.
2. realtime graphic noise-reduction method according to claim 1, is characterized in that, the preparation method of described picture noise enveloping curve comprises step:
The poor δ of noise criteria that uncalibrated image collecting device different gains is corresponding;
According to the noise reduction intensity of setting, the picture noise enveloping curve according to following Formula corresponding to input picture: NoiseThr (y, X)=max ((f
-1(f (y)+X)-y), (y-f
-1(f (y)-X)));
Wherein y is the pixel value of pixel in input picture, X is the noise intensity of pixel, X=K* δ, K is noise reduction intensity, NoiseThr (y, X) is the maximum noise that pixel is corresponding, and f (y) is mapped to the gamma mapping function that display end shows for input picture, f (y)=y^ (1/r), r is gamma coefficient.
3. realtime graphic noise-reduction method according to claim 2, it is characterized in that, each pixel for input picture, its corresponding noise threshold value is exactly on described picture noise enveloping curve, to maximum noise NoiseThr (y that should pixel pixel value, X), the pixel value that y is this pixel.
4. realtime graphic noise-reduction method according to claim 1, is characterized in that, describedly to each pixel, utilizes the neighbor pixel that participates in noise reduction in its neighborhood to carry out noise reduction process, comprises step:
For each pixel, calculate and participate in the neighbor pixel of noise reduction and the mean value of its difference;
This mean value is added on this pixel, as the pixel value after this pixel noise reduction.
5. realtime graphic noise-reduction method according to claim 1, is characterized in that, describedly to each pixel, utilizes the neighbor pixel that participates in noise reduction in its neighborhood to carry out noise reduction process, comprises step:
For each pixel, for participating in the neighbor pixel of noise reduction, distribute noise reduction weight, and calculate the long-pending of this noise reduction weight and described difference;
The mean value of calculated product, is added to this mean value on this pixel, as the pixel value after this pixel noise reduction.
6. a realtime graphic denoising device, for the image of image capture device collection is carried out to noise reduction, is characterized in that, this device comprises:
Table look-up module for each pixel for input picture, finds corresponding noise threshold value in picture noise enveloping curve corresponding to image capture device current gain value;
Detection module, for calculating the difference of neighbor pixel and this pixel in each neighborhood of pixel points, the neighbor pixel that the absolute value of difference is less than noise threshold value participates in noise reduction, otherwise does not participate in noise reduction;
Noise reduction module, for utilizing the neighbor pixel that participates in noise reduction in its neighborhood to carry out noise reduction process to each pixel.
7. realtime graphic denoising device according to claim 6, it is characterized in that, described device also comprises picture noise enveloping curve demarcating module, described picture noise enveloping curve demarcating module is for the picture noise enveloping curve of uncalibrated image collecting device, and search module described in the picture noise enveloping curve of demarcation is input to, described picture noise enveloping curve demarcating module is carried out following steps when the picture noise enveloping curve of uncalibrated image collecting device:
The poor δ of noise criteria that uncalibrated image collecting device different gains is corresponding;
According to the noise reduction intensity of setting, the picture noise enveloping curve according to following Formula corresponding to input picture: NoiseThr (y, X)=max ((f
-1(f (y)+X)-y), (y-f
-1(f (y)-X)));
Wherein y is the pixel value of pixel in input picture, X is the noise intensity of pixel, X=K* δ, K is noise reduction intensity, NoiseThr (y, X) is the maximum noise that pixel is corresponding, and f (y) is mapped to the gamma mapping function that display end shows for input picture, f (y)=y^ (1/r), r is gamma coefficient.
8. realtime graphic denoising device according to claim 7, it is characterized in that, each pixel for input picture, its corresponding noise threshold value is exactly on described picture noise enveloping curve, to maximum noise NoiseThr (y that should pixel pixel value, X), the pixel value that y is this pixel.
9. realtime graphic denoising device according to claim 6, is characterized in that, described noise reduction module, when carrying out image noise reduction processing, is carried out following steps:
For each pixel, calculate and participate in the neighbor pixel of noise reduction and the mean value of its difference;
This mean value is added on this pixel, as the pixel value after this pixel noise reduction.
10. realtime graphic denoising device according to claim 6, is characterized in that, described noise reduction module, when carrying out image noise reduction processing, is carried out following steps:
For each pixel, for participating in the neighbor pixel of noise reduction, distribute noise reduction weight, and calculate the long-pending of this noise reduction weight and described difference;
The mean value of calculated product, is added to this mean value on this pixel, as the pixel value after this pixel noise reduction.
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