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Fast parallel achieving method for non-local average filtering

A technique of non-local mean value and realization method, which is applied in image data processing, instrumentation, calculation, etc.

Active Publication Date: 2014-04-23
江苏一影医疗设备有限公司
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

The final output image is In summary, the computational complexity of the common GPU acceleration algorithm is O(|N|((2B+1)(2B+1)+2)+1)

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  • Fast parallel achieving method for non-local average filtering

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Embodiment Construction

[0042] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0043] A fast non-local mean filtering algorithm based on improved GPU parallelism, including the following steps:

[0044] Step 1. In the GPU, each thread calculates the absolute value of the gray difference between its corresponding pixel and a pixel at a certain position in the search window. After all the threads have calculated the difference values, calculate the B+1 possible grayscale accumulation values ​​of the center row of the comparison block (assuming the radius is B) centered on the ...

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Abstract

A non-local average filtering algorithm is an algorithm which is extensively used for pattern noise restraining. The algorithm constructs a weighting filter to restrain noises in images according to the hypothesis that similar neighborhood structures exist around pixels belonging to a same image structure and on the basis of neighborhood similarity. Experimental results show that the non-local average filtering algorithm can effectively restrain the noises in the images while keeping organization information of the images; in order to effectively restrain noises in the images, generally, a larger search window is required to lead into more neighborhood information, amount of computing work and processing time are required, and the application in the reality is influenced. For solving the problems, the invention provides a fast parallel achieving method for non-local average filtering. According to the invention, shared storage properties and non-local average weight symmetry are used for optimizing parallel operation on the basis of original GPU parallel using pixel as a unit, and the computation speed of the non-local average filtering algorithm is increased remarkably.

Description

technical field [0001] The invention relates to a fast parallel implementation method of a non-local mean filtering algorithm on GPU. Background technique [0002] Image noise reduction has always been an important research content in the field of digital image processing. Classical noise reduction filtering methods include neighborhood average method, median method, and some frequency domain filtering methods. These image noise reduction algorithms are generally based on the grayscale of pixels. Information such as degree difference and gradient is used, and only the information of the smaller neighborhood is used, which can easily lead to image processing results with blurred structures. And Buades proposed a non-local mean filtering algorithm based on the fact that any small window in the image can be found in a large range of the image, and many similar window structures can be found. Image information in a wide range suppresses noise, so that noise in the image can be ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 陈阳庄志昆罗立民李松毅鲍旭东
Owner 江苏一影医疗设备有限公司
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