US20160063685A1 - Image Denoising Using a Library of Functions - Google Patents
Image Denoising Using a Library of Functions Download PDFInfo
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- US20160063685A1 US20160063685A1 US14/475,806 US201414475806A US2016063685A1 US 20160063685 A1 US20160063685 A1 US 20160063685A1 US 201414475806 A US201414475806 A US 201414475806A US 2016063685 A1 US2016063685 A1 US 2016063685A1
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- 230000006870 function Effects 0.000 title claims description 47
- 238000000034 method Methods 0.000 claims abstract description 39
- 238000012549 training Methods 0.000 claims description 13
- 238000012886 linear function Methods 0.000 claims description 5
- 238000013139 quantization Methods 0.000 claims description 4
- 238000009499 grossing Methods 0.000 claims description 2
- 238000013507 mapping Methods 0.000 description 11
- 238000010586 diagram Methods 0.000 description 8
- 239000011159 matrix material Substances 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000009792 diffusion process Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 230000002146 bilateral effect Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
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- G06T5/002—
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- G06K9/4642—
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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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/50—Extraction of image or video features by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
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- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
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- 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
- G06T2207/20021—Dividing image into blocks, subimages or windows
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- 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
- G06T2207/20081—Training; Learning
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- 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
- G06T2207/20172—Image enhancement details
- G06T2207/20182—Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering
Definitions
- Another method uses anisotropic diffusion, where a shape of the filter can be adapted to local image structure as a result of a diffusion process, which is sensitive to image discontinuities.
- FIG. 1 is a flow diagram of a denoising method according to embodiments of the invention.
- FIG. 2 is a detailed flow diagram of the denoising method to embodiments of the invention.
- the patch variance estimate is approximately the lower bound of the noise variance distribution for pixels with value v.
- the noise variance of value v is estimated as k th order statistics of a distribution of noise variances for central pixels of the patches with an average value v.
- k is selected as, for example, 0.1 of the number of pixels with value v. of a distribution of variance computations of patches central pixel value
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Abstract
Description
- The invention relates generally to image processing, and more particularly to processing images to reduce noise and enhance image content.
- The goal of image denoising is to reconstruct a “noise free” enhanced image from an image corrupted with noise. Many image processing methods exist for image denoising.
- The first group of methods uses local methods where a noise free estimate of a target pixel is reconstructed as a weighted average of pixel values within a small spatial neighborhood of the target pixel. One such method convolves the noisy image with a smoothing filter such as a Gaussian kernel function where weights decrease with distance from the target pixel according to the function. Due to the linear filtering operation, which is insensitive to the local image structure (pixel values) within the neighborhood, the resulting image usually has blurry artifacts.
- To overcome the blurring artifact, other local methods attempt to make the filters “edge-aware” by using non-linear filters. A median filter reconstructs a pixel value as a median value of the pixel values within the neighborhood.
- Another method uses anisotropic diffusion, where a shape of the filter can be adapted to local image structure as a result of a diffusion process, which is sensitive to image discontinuities.
- A sigma filter identifies pixels that are similar to the target pixel within its neighborhood by thresholding the absolute pixel value difference between the target pixel and other pixels within the neighborhood. The target pixel is then reconstructed by an average of only the similar pixel values (pixels with difference within the similarity threshold) within the neighborhood. The weights in that model can be considered as being 0 or 1 according to similarity criteria.
- Several extensions to sigma filter are known. For example, one can use a bilateral filter where, instead of a hard thresholding operator (0 or 1 weights), the weights are continuously varied according to a multiplication of two kernel functions, one in spatial domain, and one in range (pixel value) domain.
- Another method examines different local image areas around the pixel of interest, and determines a noise free estimate for each local area. The estimate for the pixel of interest is then determined as some weighted average of the local area estimates.
- Another method determines a noise variance for each pixel value, which is stored in a lookup table. A similarity threshold and weights are then adjusted based on this noise model.
- Local denoising methods benefit from spatial locality, which allows fast computation. However, those methods fail to use global statistics of an image, such as repeating patterns, which is an important cue for image denoising.
- The second group of methods uses non-local denoising methods. The non-local method retrieves image patches similar to the target image patch by searching all patches within the image. Then, the target patch is replaced by a weighted average of the similar patches.
- Alternative non-local methods include sparse reconstruction of image patches using a learned dictionary from the same image, and transform domain non-local filtering. The non-local search step of those methods makes them difficult to use in systems with computational and memory constraints.
- Alternatively, non-local denoising can be achieved by performing inference on a Markov random field (MRF) using graph cuts or belief propagation. To achieve fast performance, the number of iterations is small, leaving considerable noise in the result.
- The embodiments of the invention provide a method for denoising a noisy image to generate a noise free enhanced image. The method uses a library of local denoising functions. For each pixel of the image, a key is constructed using a local neighborhood (patch) around the pixel. The key maps each pixel to a denoising function in the function library. The noise free reconstruction of the pixel is determined by applying the selected function to the patch of pixels.
- The library of denoising functions can be learned, for example, using training images in an offline process. The learning process minimizes a reconstruction error. The method combines non-linear mapping through the keys with the functions. The functions are optimized for various patch configurations to provide better reconstruction than existing hand tuned local denoising methods, while still allowing fast processing.
-
FIG. 1 is a flow diagram of a denoising method according to embodiments of the invention; -
FIG. 2 is a detailed flow diagram of the denoising method to embodiments of the invention; -
FIG. 3 is a schematic of spatial mapping according to embodiments of the invention; -
FIG. 4 is a flow diagram of noise mapping according to embodiments of the invention; -
FIG. 5 is a flow diagram of function mapping according to embodiments of the invention; -
FIG. 6 is a schematic of denoising using a library function according to embodiments of the invention; and -
FIG. 7 is a flow diagram of constructing a function library according to embodiments of the invention. -
FIG. 1 is a flow diagram of a method for denoising 110 anoisy image 101 using alibrary 501 of local denoising functions to produce adenoised image 102 according to embodiments of the invention. The method can be performed in a processor connected to memory and input/output interfaces by buses as known in the art. -
FIG. 2 is a detailed flow diagram of the denoising method. A pixel x in the noisy image has a value I(x). A local neighborhood or “patch” around the pixel is P(x). The patch can have arbitrary shapes such as a rectangle, ellipse, or an irregular arrangement of pixels adapted to the image content. -
Noise estimator 210 determines anoise variance σ 211 for each pixel using the local neighborhood or patch of pixels P(x) in thenoisy image 101. The function mapping m(x) 510 maps each pixel to afunction 230 using the noise variance estimation and a local neighborhood of the pixel, seeFIG. 5 . The selected function ƒ 230 is applied 240 to the patch P(x) in the noisy image, to generate a corresponding denoised pixel for thedenoised image 102. - During the
noise estimation 210, the method estimates the variance of the noise for each pixel. In one embodiment the variance a is assumed to be identical for all pixels having the same value v -
if I(x)=v and I(y)=v, then σ(x)=σ(y)=σv. - The variance is estimated using local patches around each pixel. For each patch in the image with mean value v, the variance of the intensity values of the patch is determined, one variance for each patch. If the patch originates from a constant color area, then the variance of this patch is equal to an empirical estimation of the noise.
- However, because the patch can also originate from a non-constant color area, the patch variance estimate is approximately the lower bound of the noise variance distribution for pixels with value v. The noise variance of value v is estimated as kth order statistics of a distribution of noise variances for central pixels of the patches with an average value v. In one embodiment, k is selected as, for example, 0.1 of the number of pixels with value v. of a distribution of variance computations of patches central pixel value
- In general, the variance changes smoothly with changing pixel values. Independent noise estimation for each value results in non-smooth noise profiles. Therefore, the variances are smoothed to produce smooth noise profile.
- Function Mapping
- For each pixel x of the image, a key is constructed using the patch P(x) around the pixel x and the noise estimate for the pixel value. The key includes a spatial key, and a noise key.
- As shown in
FIG. 3 thespatial key 302 is constructed by thespatial mapping function 310 using a patch P(x) 301 of, e.g., 3×3, pixels I(xi), xiεP(x) around the pixel x. The size and the shape of the patch P(x) can be different than the patch that was used to estimate the variance of the noise. - In one embodiment, the spatial key is a local n-ary pattern (LnP), 320 for example a local binary pattern (LbP). For each pixel xi within the patch one bit of information is acquired. If a difference between the pixel value I(xi) in the patch and the pixel value I(x) in the noisy image is smaller than the variance for the pixel σ(x), then the bit is set to 0 otherwise the bit is set 321 to 1:
-
- The spatial key has bits. Examples of determined local binary patterns are shown in
FIG. 3 where black pixels (0 bits) correspond to pixels within the patch having similar values to the pixel in the noisy image, i.e., the center pixel in the patch, and white pixels (1 bits) correspond to pixels within the patch having dissimilar values to the pixel in the noisy image. -
FIG. 4 is a flow diagram of the noise mapping. Anoise key 402 is constructed by thenoise mapping function 410 using the estimation of thevariance 401 for the pixel in the noisy image. In one embodiment, the noise key is given by an n bit uniform quantization of variance σ(x): -
n(x)=|n| bit quantization of σ(x). - The key m for the pixel is a concatenation of the spatial key and the noise key as follows:
-
- such that the key has |s|+|n| bits.
- As shown in
FIG. 5 , thekey m 502 maps 510 a pixel of the image to a denoising function ƒ in thedenoising function library 501 that is used to denoise the pixel. - Denoising Using a Library Function
- As shown in
FIG. 6 , thekey k 502 is determined from noisy image pixel x, and is the corresponding function in the function library. The denoising function uses a patch P(x) 601 around the pixel, i.e., the patch includes locally neighboring pixels around the pixel x. The size and the shape of this patch can be different than the patch that was used to determine the key. In one embodiment the denoising function is a linear function of the pixel intensity in the patch - Learning Denoising Function Library
- As shown in
FIG. 7 , thedenoising function library 501 is learned usingtraining image samples 721.Training image samples 721 include noise free 701 and corresponding noisy 711 training image sample pairs. Thetraining samples 721 are generated by the training samples constructor 720. In one embodiment, the noisy 711 images are acquired by adding 710 synthetic noise to the noise free 701 images. - The training or function fitting 730 optimizes the functions such that a difference between a reconstruction of the noise free image using the noisy 711 image and the noise free 701 image is minimized:
-
- where I and Ĩ are the noise free and noisy training image sample pairs 721, l is the number of denoising functions in the function library and denoising function F operates per image pixel by first mapping the pixel to the denoising function using the
function mapping 510 and then uses the mapped function to denoise the pixel: -
F(x)=ƒm(x)(x). - In one embodiment, the training is solved by grouping the pixels of the training image pairs according to the keys. Then, each group is optimized separately. When the library functions are linear functions they are learned optimally by solving a linear least squares problem. When they are non-linear functions, they are learned using non-linear optimization techniques such as gradient descent or Newton's method.
- Although the invention has been described by way of examples of preferred embodiments, it is to be understood that various other adaptations and modifications can be made within the spirit and scope of the invention. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the invention.
Claims (12)
n(x)=|n| bit quantization of σ(x).
Priority Applications (5)
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US14/475,806 US9262810B1 (en) | 2014-09-03 | 2014-09-03 | Image denoising using a library of functions |
CN201580045301.9A CN106663315B (en) | 2014-09-03 | 2015-08-28 | Method for denoising noisy images |
PCT/JP2015/004374 WO2016035305A1 (en) | 2014-09-03 | 2015-08-28 | Method for denoising noisy image |
GB1703366.3A GB2544233B (en) | 2014-09-03 | 2015-08-28 | Method for denoising noisy image |
JP2016567938A JP6239153B2 (en) | 2014-09-03 | 2015-08-28 | Method for removing noise from images with noise |
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Cited By (5)
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US9760978B1 (en) * | 2016-05-09 | 2017-09-12 | Adobe Systems Incorporated | Missing region prediction |
US9911201B2 (en) | 2016-06-23 | 2018-03-06 | Adobe Systems Incorporated | Imaging process initialization techniques |
CN108665427A (en) * | 2018-04-17 | 2018-10-16 | 浙江华睿科技有限公司 | A kind of image denoising method and device |
US10970582B2 (en) * | 2018-09-07 | 2021-04-06 | Panasonic Intellectual Property Corporation Of America | Information processing method, information processing device, and recording medium |
US11593918B1 (en) | 2017-05-16 | 2023-02-28 | Apple Inc. | Gradient-based noise reduction |
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RU2764395C1 (en) | 2020-11-23 | 2022-01-17 | Самсунг Электроникс Ко., Лтд. | Method and apparatus for joint debayering and image noise elimination using a neural network |
CN113643198A (en) * | 2021-07-22 | 2021-11-12 | 海宁奕斯伟集成电路设计有限公司 | Image processing method, image processing device, electronic equipment and storage medium |
CN115829874A (en) * | 2022-03-31 | 2023-03-21 | 南通电博士自动化设备有限公司 | Noise processing method based on image smoothing |
CN116152115B (en) * | 2023-04-04 | 2023-07-07 | 湖南融城环保科技有限公司 | Garbage image denoising processing method based on computer vision |
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CN100505832C (en) * | 2006-03-21 | 2009-06-24 | 中国科学院计算技术研究所 | Image de-noising process of multi-template mixed filtering |
JP5061882B2 (en) * | 2007-12-21 | 2012-10-31 | ソニー株式会社 | Image processing apparatus, image processing method, program, and learning apparatus |
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- 2014-09-03 US US14/475,806 patent/US9262810B1/en not_active Expired - Fee Related
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2015
- 2015-08-28 CN CN201580045301.9A patent/CN106663315B/en not_active Expired - Fee Related
- 2015-08-28 GB GB1703366.3A patent/GB2544233B/en not_active Expired - Fee Related
- 2015-08-28 JP JP2016567938A patent/JP6239153B2/en not_active Expired - Fee Related
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Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9760978B1 (en) * | 2016-05-09 | 2017-09-12 | Adobe Systems Incorporated | Missing region prediction |
US9911201B2 (en) | 2016-06-23 | 2018-03-06 | Adobe Systems Incorporated | Imaging process initialization techniques |
US11593918B1 (en) | 2017-05-16 | 2023-02-28 | Apple Inc. | Gradient-based noise reduction |
CN108665427A (en) * | 2018-04-17 | 2018-10-16 | 浙江华睿科技有限公司 | A kind of image denoising method and device |
US10970582B2 (en) * | 2018-09-07 | 2021-04-06 | Panasonic Intellectual Property Corporation Of America | Information processing method, information processing device, and recording medium |
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WO2016035305A1 (en) | 2016-03-10 |
GB2544233B (en) | 2020-02-26 |
GB201703366D0 (en) | 2017-04-19 |
CN106663315A (en) | 2017-05-10 |
CN106663315B (en) | 2020-07-07 |
JP6239153B2 (en) | 2017-11-29 |
JP2017516233A (en) | 2017-06-15 |
GB2544233A (en) | 2017-05-10 |
US9262810B1 (en) | 2016-02-16 |
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