Recent Patents in Signal Processing (July 2017) – Image denoising

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Recent Patents in Signal Processing (July 2017) – Image denoising

For our July 2017 issue, we cover recent patents granted in the area of image denoising.

Patent no. 9,659,352 presents a method, computer program product, and computer system for identifying a first portion of a facial image in a first image, wherein the first portion includes noise. A corresponding portion of the facial image is identified in a second image, wherein the corresponding portion includes less noise than the first portion. One or more filter parameters of the first portion are determined based upon, at least in part, the first portion and the corresponding portion. At least a portion of the noise from the first portion is smoothed based upon, at least in part, the one or more filter parameters. At least a portion of face specific details from the corresponding portion is added to the first portion.

Patent no. 9,576,346 introduces a system, apparatus, method, and computer readable media for edge-enhanced non-local means (NLM) image denoising. In embodiments, edge detail is preserved in filtered image data by weighting of the noisy input target pixel value with other pixel values based on self-similarity and further informed by a data-driven directional spatial filter. Embodiments herein may denoise regions of an image lacking edge characteristics with a more uniform spatial filter than those having edge characteristics. In embodiments, directionality of a spatial filter function is modulated based on an edge metric to increase the weighting of pixel values along an edge when there is a greater probability the edge passes through the target pixel. In further embodiments, the adaptive spatial filter is elliptical and oriented relative to a spatial gradient direction with non-uniform filter widths that are based on the edge metric

Invention no. 9,558,537 provides an image denoising method and an image denoising apparatus. The image denoising method includes performing preliminary denoising processing to an acquired image to be processed, so as to obtain a preliminarily denoised image; calculating a residual quantity corresponding to a central pixel of each unit area in the image to be processed according to numerical values of specific energy parameters to which the image to be processed and the preliminarily denoised image correspond, respectively; and using the residual quantity to calculate a weight matrix corresponding to each unit area, and performing non-local mean value calculation to the image to be processed according to the weight matrix, so as to realize the denoising processing of the image to be processed. The image denoising method is able to denoise effectively, and make a denoised image more visually natural.

A method for implementing image denoising is providedin patent no. 9,489,722. The method includes: calculating a tangent value of each pixel; determining whether a modulus value of the tangent value of each pixel is less than a preset threshold, if yes, determining a corresponding pixel as a non-boundary point of the image, and performing bilateral filter on a pixel determined as a non-boundary point of the image and pixels which are around the pixel and of which distances to the pixel are less than or equal to a first filtering radius; and if not, determining a corresponding pixel as a boundary point of the image, and performing bilateral filter on a pixel determined as a boundary point of the image and pixels whose distances along tangent directions and opposite directions of the tangent directions to the pixel are less than or equal to a second filtering radius.

In patent no. 9,489,720 system, apparatus, method, and computer readable media for texture enhanced non-local means (NLM) image denoising. In embodiments, detail is preserved in filtered image data through a blending between the noisy input target pixel value and the NLM pixel value that is driven by self-similarity and further informed by an independent measure of local texture. In embodiments, the blending is driven by one or more blending weight or coefficient that is indicative of texture so that the level of detail preserved by the enhanced noise reduction filter scales with the amount of texture. Embodiments herein may thereby denoise regions of an image that lack significant texture (i.e. are smooth) more aggressively than more highly textured regions. In further embodiments, the blending coefficient is further determined based on similarity scores of candidate patches with the number of those scores considered being based on the texture score.

A method presented in patent no. 9,262,810 denoises a noisy image by, for each pixel in the noisy image, first constructing a key from a patch, wherein the patch includes locally neighboring pixels around the pixel. A function is selected from a function library using the key. Then, the function is applied to the patch to generate a corresponding noise free pixel for the pixel.

The disclosure no. 9,230,161 provides a method, system and computer program product for denoising an image by extending a Block Matching and 3D Filtering algorithm to include decomposition of high contrast image blocks into multiple layers that are collaboratively filtered. According to an exemplary method, the high contrast image blocks are decomposed into a top layer, a bottom layer and a mask layer.

Systems and methods for multispectral imaging are disclosed in patent no. 9,171,355. The multispectral imaging system can include a near infrared (NIR) imaging sensor and a visible imaging sensor. The disclosed systems and methods can be implemented to de-noise a visible light image using a gradient scale map generated from gradient vectors in the visible light image and a NIR image. The gradient scale map may be used to determine the amount of de-noising guidance applied from the NIR image to the visible light image on a pixel-by-pixel basis.

If you have an interesting patent to share when we next feature patents related to image denoising, or if you are especially interested in a signal processing research field that you would want to be highlighted in this section, please send email to Csaba Benedek (benedek.csaba AT sztaki DOT mta DOT hu).


Title:  Image denoising system and method
Inventors:  Ioffe; Sergey (Mountain View, CA), Chinen; Troy (Neward, CA), Kwatra; Vivek (Mountain View, CA), Fang; Hui (Mountain View, CA), Shih; Yichang (San Jose, CA)
Issued:  May 23, 2017
Assignee: Google Inc. (Mountain View, CA)

Title: Non-local means image denoising with an adaptive directional spatial filter
Inventors:  Cohen; Anna (Petach-Tikva, IL)
Issued:  February 21, 2017
Assignee: Intel Corporation (Santa Clara, CA)

Title:  Image denoising method and image denoising apparatus
Inventors:  Wu; Jianrong (Beijing, CN), Tan; Zhiming (Beijing, CN), Higashi; Akihiro (Beijing, CN)
Issued:  January 31, 2017
Assignee:  Fujitsu Limited (Kawasaki, JP)

Title: Method and apparatus for implementing image denoising
Inventors:  Jiang; Deqiang (Guangdong, CN)
Issued: November 8, 2016
Assignee: Tencent Technology (Shenzhen) Company Limited (Shenzhen, CN)

Title: Non-local means image denoising with detail preservation using self-similarity driven blending
Inventors: Oron; Shaul (Petach-Tikva, IL), Michael; Gilad (Tzur Yizhak, IL)
Issued:  November 8, 2016
Assignee:  Intel Corporation (Santa Clara, CA)

Title:  Image denoising using a library of functions
Inventors: Tuzel; Oncel (Winchester, MA), Thornton; Jay (Watertown, MA), van Baar; Jeroen (Arlington, MA)
Issued:  February 16, 2016
Assignee: Mitsubishi Electric Research Laboratories, inc. (Cambridge, MA)

Title: Multiple layer block matching method and system for image denoising
Inventors:  Fan; Zhigang (Webster, NY)
Issued:  January 5, 2016
Assignee:  Xerox Corporation (Norwalk, CT)

Title:  Near infrared guided image denoising
Inventors:  Zhuo; Shaojie (Markham, CA), Zhang; Xiaopeng (Richmond Hill, CA), Feng; Chen (Markham, CA), Shen; Liang (Toronto, CA), Jia; Jiaya (Hong Kong, HK)
Issued:  October 27, 2015
Assignee:  Qualcomm Incorporated (San Diego, CA)

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