GLOBAL AND LOCAL STATISTICS CONTROLLED NOISE REDUCTION SYSTEM
A global and local statistics controlled noise reduction system in which the video image noise reduction processing is effectively adaptive to both image local structure and global noise level. A noise estimation method provides reliable global noise statistics to the noise reduction system. The noise reduction system dynamically/adaptively configures a local filter for processing each image pixel, and processes the pixel with that local filter. The filtering process of the noise reduction system is controlled by both global and local image statistics that are also computed by the system.
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The present invention relates generally to video image processing, and more particularly to noise reduction in video images.
BACKGROUND OF THE INVENTIONVideo images are often corrupted by noise during the video image acquisition or transmission process. In order to improve the visual appearance of such images, an effective filtering technique is often required to reduce the noise level therein. Traditional noise reduction techniques mainly involve applying a linear filter such as an averaging filter to all of the pixels in a video frame (“image”). While this reduces noise level in the image, such a linear filtering technique also indiscriminately blurs edges in the image.
In order to prevent image edge blurring, a noise reduction filter must be adaptive to local structures, such as edges, in the image. One such adaptive technique is known as directional filtering. Directional filtering attempts to avoid image blurring by adapting linear filtering to image edge directions in such a way that the filter utilized is always applied along the edge direction not across the edge direction.
There are two major shortcomings to the directional filtering technique. The first is that the threshold value must be manually tuned and usually it is difficult to select the right value. An improperly selected threshold value will cause either image blurring or insufficient noise reduction. The second shortcoming of the directional filter is that the filter strength is fixed. That means a relatively clean image is processed the same way as a highly noisy image. This causes the relatively clean image to unnecessarily lose some fine structures and be degraded.
BRIEF SUMMARY OF THE INVENTIONThe present invention addresses the above shortcomings. As such, in one embodiment the present invention provides a global and local statistics controlled noise reduction system wherein the video image noise reduction processing is effectively adaptive to both image local structure and global noise level. And, a noise estimation method according to the present invention provides reliable global noise statistics to the noise reduction system.
Such a global and local statistics controlled noise reduction system dynamically/adaptively configures a local filter for processing each image pixel, and processes the pixel with that local filter. The filtering process of the noise reduction system is controlled by both global and local image statistics that are also computed by the system. In one example, the local statistics computed by the system are 1-D and 2-D local variances, and the global statistics computed by the system is the global noise standard deviation. The local filter configured by the system for each image pixel has different filtering directions and variable strength for different pixels. The direction of the local filter is determined by 1-D local variances. The strength of the local filter is computed directly from the local variances and the global noise standard deviation.
According to a further aspect of the present invention, the global noise standard deviation is estimated by a noise estimation method. First, the image is divided into overlapping or non-overlapping blocks, and the mean and the standard deviation of each block are calculated. Then, the smallest standard deviation is found together with the corresponding block mean. After the smallest standard deviation and its corresponding mean have been found, a “saturation checking” process is applied to determine whether the block with the smallest standard deviation has saturated. This determination is based on the relation between the smallest standard deviation and its corresponding mean.
If saturation is not detected, the calculated block standard deviations that are within a neighboring interval centered at the smallest standard deviation are averaged, and the average value is taken as the estimated global noise standard deviation. The radius of the neighboring interval depends on the value of the smallest standard deviation.
If saturation is detected, first a saturation compensation term is added to the smallest standard deviation to generate a compensated smallest standard deviation. The saturation compensation term is computed from the smallest standard deviation and its corresponding mean. Then, the calculated block standard deviations that are within a neighboring interval centered at the compensated smallest standard deviation are averaged to obtain the estimated global noise standard deviation. The noise standard deviation is used in the noise reduction system.
Other objects, features and advantages of the present invention will be apparent from the following specification taken in conjunction with the following drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
As noted above, in one embodiment the present invention provides a global and local statistics controlled noise reduction system wherein the video image noise reduction processing is effectively adaptive to both image local structure and global noise level. And, a noise estimation method according to the present invention provides reliable global noise statistics to a noise reduction system. The system dynamically configures a local filter for processing each image pixel, and processes the pixel with that local filter. The filtering process is controlled by both global and local image statistics. In one example, the local statistics computed by the system are 1-D and 2-D local variances, and the global statistics computed by the system is the global noise standard deviation. The dynamically configured local filter has different directions and variable strength for different pixels. The direction of the local filter is determined by 1-D local variances. The strength of the local filter is computed directly from the local variances and the global noise standard deviation.
The global noise standard deviation is estimated by a preferred noise estimation method that comprises the following steps. First, the image is divided into overlapping or non-overlapping blocks, and the mean and the standard deviation of each block are calculated. Then, the smallest standard deviation is found together with the corresponding block mean. After the smallest standard deviation and its corresponding mean have been found, a “saturation checking” process is applied to determine whether the block with the smallest standard deviation has saturated. The process is based on the relation between the smallest standard deviation and its corresponding mean.
If no saturation is detected, the calculated block standard deviations that are within a neighboring interval centered at the smallest standard deviation are averaged, and the average value is taken as the estimated global noise standard deviation. The radius of the neighboring interval depends on the value of the smallest standard deviation. If saturation is detected, first a saturation compensation term is added to the smallest standard deviation to generate a compensated smallest standard deviation. The saturation compensation term is computed from the smallest standard deviation and its corresponding mean. Then, the calculated block standard deviations that are within a neighboring interval centered at the compensated smallest standard deviation are averaged to obtain the estimated global noise standard deviation (the radius of the neighboring interval depends on the value of the compensated smallest standard deviation). The global noise standard deviation is used in the noise reduction system.
An example of the noise reduction system according to the present invention is now described.
In the example of
If k=0, then σ02 is the 2-D variance, and is computed by:
where μ0 is the corresponding 2-D mean, defined as:
If k>0, then σk2 (k=1, 2, 3, 4) are 1-D variances along the direction Lk (k=1, 2, 3, 4), and are computed by:
σ12=((P(i, j−1)−μ1)2+(P(i, j)−μ1)2+(P(i, j+1) μ1)2)3
σ22=((P(i−1, j)−μ2)2+(P(i, j)−μ2)2+(P(i+1, j) μ2)2)3
σ32=((P(i−1, j−1)−μ3)2+(P(i, j)−μ3)2+(P(i+1, j+1)−μ3)2)/3;
σ42=((P(i−1, j+1)−μ4)2+(P(i, j)−μ4)2+(P(i+1, j−1)−μ4)2)/3;
where μk (k=1, 2, 3, 4) are the means along the direction Lk (k=1, 2, 3, 4), and are computed by:
μ1=(P(i, j−1)+P(i, j)+P(i, j+1))/3;
μ2=(P(i−1, j)+P(i, j)+P(i+1, j))/3;
μ3=(P(i−1, j−1)+P(i, j)+P(i+1, j+1))/3;
μ4=(P(i−1, j+1)+P(i, j)+P(i+1, j−1))/3;
After computing the local variances, the Local Statistics unit 220 (
The Filter Generator 240 generates a local filter for the pixel to be filtered. The direction of the local filter is the local edge direction detected by the Direction Detector 230. The strength of the local filter is computed by using the global noise standard deviation σ provided by the Global Statistics unit 210 and the local variances σk2 (k=0, 1, 2, 3, 4) provided by the Local Statistics unit 220. For edge direction Lk (k=1, 2, 3, 4), the designation αk (k=1, 2, 3, 4) denotes the corresponding filter strength along those directions. Further, the designation α0 denotes the filter strength for non-edge area filtering. While αk (k=1, 2, 3, 4) controls the strength for filtering along the edge direction, α0 controls the strength for non-edge area filtering.
The filter strengths αk (k=1, 2, 3, 4) for edge direction Lk (k=1, 2, 3, 4) are functions of the global noise standard deviation and the local variance, and are computed in one example as:
αk=min(2σ, max(3σ−σk,0))/(2σ);
wherein min(a,b) is the minimal function that returns the smaller one of the two values a and b, and max(a,b) is the maximal function that returns the larger one of the two values a and b.
The filter strength α0 for non-edge area is computed similarly as following:
α0=min(2σ, max(3σ−σ0,0))/(2σ).
The curve for α0 is similar to that for αk (k=1, 2, 3, or 4) shown in
Using the detected local edge direction Lk (k=1, 2, 3, or 4), the edge direction filter strengths αk (k=1, 2, 3, or 4), and the non-edge area filter strength α0 computed above, the Filter Generator unit 240 (
If the detected direction is L1, then f1 is a 2-D local filter for horizontal direction, and is defined as:
If the detected direction is L2, then f2 is a 2-D local filter for vertical direction, and is defined as:
If the detected direction is L3, then f3 is a 2-D local filter for the diagonal direction from upper left to lower right, and is defined as:
and
If the detected direction is L4, then f4 is a 2-D local filter for the diagonal direction from upper right to lower left, and is defined as:
The generated local filter fk is supplied to the Pixel Filtering unit 250 (
As noted, the Global Statistics unit 210 estimates the global noise statistics using a preferred noise estimation method. Different methods have been proposed to estimate the noise present in the images, such as those described in the paper by S. I. Olsen: “Noise Variance Estimation in Images: An Evaluation”, Graphical Models and Image Processing, vol. 55, no. 4, pp. 319-323, 1993.
However, existing methods have not properly considered saturation effects (i.e., pixel values driven into saturation (0 or 255) by the noise, causing inaccurate estimates (in most cases underestimates) of noise). Inaccurate noise estimate can have serious impact on the performance of the noise reduction system that is controlled by global noise statistics.
The computed block standard deviations and means are then provided to a Minimal Finder 520. The block standard deviations are also provided to a Selective Averaging unit 530.
The Minimal Finder 520 finds the smallest standard deviation, and records the smallest standard deviation and its corresponding block mean as d0 and m0, respectively, wherein the values d0 and m0 are then supplied to a Saturation Checker 540.
The Saturation Checker 540 checks whether saturation has occurred in the block with the smallest standard deviation d0. That is, the Saturation Block checker determines/detects if pixel values in the block are driven into saturation (e.g., 0 or 255) due to noise, which may cause inaccurate estimates of image noise. If saturation has occurred, a Saturation Compensator 550 compensates for d0.
Examples of saturation detection criteria and the compensation methods are provided below in conjunction with
-
- For this example an upper limit UL=255, a lower limit LL=0 and a mid value M=128. Such that, if m0<128 and d0>m0−0, then saturation is detected at the lower limit 0 (
FIG. 6A ). In this case d0 is compensated as {tilde over (d)}0=d0+K·(d0−(m0−0)), wherein {tilde over (d)}0 denotes the compensated smallest standard deviation; If m0≧128 and d0>255−m0, then saturation is detected at the upper limit 255 (FIG. 6B ). In this case d0 is compensated as {tilde over (d)}0=d0+K·(d0−(255−m0)); Otherwise, no saturation is detected. In this case no compensation is needed for d0, therefore {tilde over (d)}0=d0.
- For this example an upper limit UL=255, a lower limit LL=0 and a mid value M=128. Such that, if m0<128 and d0>m0−0, then saturation is detected at the lower limit 0 (
In the above expressions for {tilde over (d)}0, the compensation parameter K is empirically determined. Preferably in this example K=5.0 is used. As those skilled in the art will recognize, the saturation detection method can be easily generalized to other situations where the images are represented by different bit values and therefore have different values for UL, LL and M.
The compensated smallest standard deviation {tilde over (d)}0 is then supplied to the Selective Averaging unit 530 (
The Selective Averaging unit 530 (
While this invention is susceptible of embodiments in many different forms, there are shown in the drawings and will herein be described in detail, preferred embodiments of the invention with the understanding that the present disclosure is to be considered as an exemplification of the principles of the invention and is not intended to limit the broad aspects of the invention to the embodiments illustrated. The aforementioned systems 200 and 500 according to the present invention can be implemented in many ways, such as program instructions for execution by a processor, as logic circuits, as ASIC, as firmware, etc., as is known to those skilled in the art. Therefore, the present invention is not limited to the example embodiments described herein.
The present invention has been described in considerable detail with reference to certain preferred versions thereof; however, other versions are possible. Therefore, the spirit and scope of the appended claims should not be limited to the description of the preferred versions contained herein.
Claims
1. (canceled)
2. The method of claim 3, wherein the step of computing the global statistics comprises the step of estimating the global noise standard deviation σ to generate the global statistics.
3. A method for reducing noise in a digital image formed from a plurality of pixels including a given pixel, the method comprising the steps of:
- computing global statistics from the image;
- computing local statistics for the given pixel;
- configuring a local filter using the local and global statistics; and
- filtering the given pixel using the local filter to reduce image noise,
- wherein the step of computing the local statistics for the given pixel further includes the steps of: selecting a window containing the given pixel and a plurality of neighboring pixels; computing a 2-D local variance of the given pixel based on information related to the pixels in the window; computing a plurality of 1-D local variances along multiple directions through the given pixel in the window; and
- detecting a local edge direction by selecting one of the directions with the smallest 1-D local variance.
4. The method of claim 3, wherein the step of computing the local statistics for the given pixel further includes the steps of:
- selecting a window containing the given pixel and a plurality of neighboring pixels;
- computing the 2-D local variance σ02 of the given pixel based on information related to the pixels in the window;
- computing the 1-D local variances σ12, σ22, σ32, and σ42 along the horizontal (L1), vertical (L2), diagonal from upper left to lower right (L3), and diagonal from upper right to lower left (L4) directions through the given pixel, respectively, in the window; and
- detecting the local edge direction by selecting the direction with the smallest 1-D local variance.
5.-13. (canceled)
14. The system of claim 15, wherein the global statistics module estimates a global noise standard deviation σ to generate the global statistics.
15. A noise reduction system for reducing noise in a digital image comprising pixels the system comprising:
- a global statistics module that computes global statistics from the image;
- a local statistics module that computes local statistics for each of a plurality of image pixels including a given pixel;
- a filter configuration module that uses the local and global statistics for the given pixel to configure a local filter for filtering the given pixel; and
- the local filter as configured by the filter configuration module being adapted for filtering the given pixel to reduce image noise, wherein the local statistics module computes the local statistics for the given pixel by: selecting a window containing the given pixel and a plurality of neighboring pixels; computing a 2-D local variance of said pixel based on information related to the pixels in the window; computing a plurality of 1-D local variances along multiple directions each defined by a pair of the pixels in the window; and detecting a local edge direction for the given pixel by selecting one of the directions with the smallest 1-D local variance.
16. The system of claim 15, wherein the local statistics module computes the local statistics for each pixel by:
- selecting a window containing said pixel and a plurality of neighboring pixels;
- computing the 2-D local variance σ02 of the given pixel based on information related to the pixels in the window;
- computing the 1-D local variances σ12, σ22, σ32, and σ42 along the horizontal (L1), vertical (L2), diagonal from upper left to lower right (L3), and diagonal from upper right to lower left (L4) directions through the given pixel, respectively, in the window; and
- detecting the local edge direction by selecting the direction with the smallest 1-D local variance.
17.-24. (canceled)
25. A method for reducing noise in a digital image at a selected pixel, comprising:
- computing global statistics from the digital image;
- computing local statistics for the selected pixel by: selecting a window containing the selected pixel and a plurality of neighboring pixels; computing a 2-D local variance of the selected pixel based on the plurality of neighboring pixels in the window; computing a plurality of 1-D local variances along multiple directions through the selected pixel in the window; and identifying a local edge direction by selecting one of the multiple directions with the smallest 1-D local variance;
- configuring a local filter using the computed local and global statistics after identifying the local edge direction; and
- filtering the given pixel using the local filter to reduce image noise.
26. The method of claim 25, wherein computing the global statistics comprises estimating a global noise standard deviation σ.
27. The method of claim 25, wherein computing the plurality of 1-D local variances along multiple directions through the selected pixel in the window comprises:
- computing the 1-D local variances σ12, σ22, σ32, and σ42 along the horizontal (L1), vertical (L2), diagonal from upper left to lower right (L3), and diagonal from upper right to lower left (L4) directions through the selected pixel, respectively, in the window.
Type: Application
Filed: Sep 11, 2007
Publication Date: Jan 3, 2008
Applicant: Samsung Electronics Co., Ltd. (Suwon City)
Inventors: Peng Lin (Pleasanton, CA), Yeong-Taeg Kim (Irvine, CA)
Application Number: 11/853,655
International Classification: G06K 9/40 (20060101);