Recent Patents in Signal Processing (December 2014) – Image Denoising

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Recent Patents in Signal Processing (December 2014) – Image Denoising

For our December 2014 issue, we cover recent patents granted in the area of image denoising. The section below covers patents granted recently for image de-bluring, enhancement layer video coding,  enhanced MRI signal reconstruction, image noise removal, and anisotropic denoising.

An image de-blurring system presented in invention no. 8,897,588 obtains a blurred input image and generates, based on the blurred input image, a blur kernel. The blur kernel is an indication of how the image capture device was moved and/or how the subject captured in the image moved during image capture, resulting in blur. Based on the blur kernel and the blurred input image, a de-blurred image is generated. The blur kernel is generated based on sharp versions of the blurred input image predicted using a data-driven approach based on a collection of prior edges.

In patent no. 8,897,359 techniques and tools for encoding enhancement layer video with quantization that varies spatially and/or between color channels are presented, along with corresponding decoding techniques and tools. For example, an encoding tool determines whether quantization varies spatially over a picture, and the tool also determines whether quantization varies between color channels in the picture. The tool signals quantization parameters for macroblocks in the picture in an encoded bit stream. In some implementations, to signal the quantization parameters, the tool predicts the quantization parameters, and the quantization parameters are signaled with reference to the predicted quantization parameters. A decoding tool receives the encoded bit stream, predicts the quantization parameters, and uses the signaled information to determine the quantization parameters for the macroblocks of the enhancement layer video. The decoding tool performs inverse quantization that can vary spatially and/or between color channels.

Magnetic susceptibility is the physical property for T2*-weighted magnetic resonance imaging (T2*MRI). The invention no. 8,886,283 relates to methods for reconstructing an internal distribution (3D map) of magnetic susceptibility values of an object, from 3D MRI phase images, by using Computed Inverse Magnetic Resonance Imaging (CIMRI) tomography. The CIMRI technique solves the inverse problem of the 3D convolution by executing a 3D Total Variation (TV) regularized iterative convolution scheme, using a split Bregman iteration algorithm. The reconstruction of magnetic susceptibility can be designed for low-pass, band-pass, and high-pass features by using a convolution kernel that is modified from the standard dipole kernel. Multiple reconstructions can be implemented in parallel, and averaging the reconstructions can suppress noise. 4D dynamic magnetic susceptibility tomography can be implemented by reconstructing a 3D susceptibility volume from a 3D phase volume by performing 3D CIMRI magnetic susceptibility tomography at each snapshot time.

An image denoising method from patent no. 8,885,965 includes the steps of: sequentially selecting a pixel in an image as a current pixel; dynamically determining a current search block and a strength parameter; pre-denoising the comparison block of each pixel in the current search block; comparing the comparison block of the pre-denoised neighborhood pixel and the comparison block of the pre-denoised current pixel to obtain a similarity between each neighborhood pixel and the current pixel in the current search block; determining a weighting of each neighborhood pixel related to the current pixel according to the strength parameter, and a distance and the similarity between each neighborhood pixel and the current pixel in the current search block; and weighted averaging each neighborhood pixel and the current pixel in the current search block according to the weighting to obtain a reconstruction value of the current pixel.

In patent no. 8,885,961 a method is presented for removing noise from an image receiving image data including a plurality of pixels. A graph including a plurality of nodes and a plurality of edges interconnecting the nodes is formulated. Each pixel of the image data is represented as a node of the graph and each edge of the graph is assigned a weight based on a penalty function applied to the nodes connected by the edge where the penalty function is less when a value of a given pixel of the plurality of pixels is between or equal to the values of two neighboring pixels than when the value of the given pixel is either greater than or less than the values of both of the two neighboring pixels. A total penalty of the graph is minimized. A denoised image is provided based on the total penalty-minimized graph.

Finally in accordance with an embodiment of the invention no. 8,879,841, an anisotropic denoising method is provided that removes sensor noise from a digital image while retaining edges, lines, and details in the image. In one embodiment, the method removes noise from a pixel of interest based on the detected type of image environment in which the pixel is situated. If the pixel is situated in an edge/line image environment, then denoising of the pixel is increased such that relatively stronger denoising of the pixel occurs along the edge or line feature. If the pixel is situated in a detail image environment, then denoising of the pixel is decreased such that relatively less denoising of the pixel occurs so as to preserve the details in the image. In one embodiment, detection of the type of image environment is accomplished by performing simple arithmetic operations using only pixels in a 9 pixel by 9 pixel matrix of pixels in which the pixel of interest is situated. As a result, improved image environment sensitive noise reduction is achieved that requires a relatively low gate count in hardware implementations.

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).

References

Number: 8,897,588
Title: Data-driven edge-based image de-blurring
Inventors:  J. Wang, S. Cho and L. Sun
Issued:  November 25, 2014
Assignee:  Adobe Systems Incorporated (San Jose, CA)

Number: 8,897,359
Title:  Adaptive quantization for enhancement layer video coding
Inventors: S. Regunathan, S. Sun, C. Tu, C-L. Lin
Issued:  November 25, 2014
Assignee:  Microsoft Corporation (Redmond, WA)

Number: 8,886,283
Title:  3D and 4D magnetic susceptibility tomography based on complex MR images
Inventors: Z. Chen and V.D. Calhoun
Issued:  November 11, 2014
Assignee: STC.UNM (Albuquerque, NM)

Number: 8,885,965
Title: Image denoising method
Inventors:  S.S. Yang, W.H. Yao and C.H. Lee
Issued:  November 11, 2014
Assignee:  PixArt Imaging Inc (Hsin-Chu County, TW)

Number: 8,885,961
Title:  System and method for image denoising optimizing object curvature  
Inventors: N. Y. El-Zehiry and L. Grady
Issued:  November 11, 2014
Assignee: Siemens Aktiengesellschaft (Munich, DE)   )

Number: 8,879,841
Title:  Anisotropic denoising method
Inventors:  N.  Cohen, J. Danowitz and O. Liba
Issued:  November 4, 2014
Assignee: Fotonation Limited (Ballybrit, Galway, IE

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