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Method for realizing acceleration of anisotropic diffusion filtration of overlarge synthetic aperture radar (SAR) image by graphic processing unit (GPU)

An anisotropic and diffusion filtering technology, applied in image enhancement, image data processing, instruments, etc., can solve the problem of slow serial processing speed and achieve the effect of solving the shortage of quantity

Inactive Publication Date: 2011-05-25
XIDIAN UNIV
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Problems solved by technology

[0007] The purpose of the present invention is to overcome the deficiencies of the above-mentioned problems, propose a super large SAR image anisotropic diffusion SRAD filtering acceleration method realized by GPU, to solve the problem of serial processing speed when performing anisotropic diffusion SRAD filtering to super large SRA images slower problem

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  • Method for realizing acceleration of anisotropic diffusion filtration of overlarge synthetic aperture radar (SAR) image by graphic processing unit (GPU)
  • Method for realizing acceleration of anisotropic diffusion filtration of overlarge synthetic aperture radar (SAR) image by graphic processing unit (GPU)
  • Method for realizing acceleration of anisotropic diffusion filtration of overlarge synthetic aperture radar (SAR) image by graphic processing unit (GPU)

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[0031] The present invention adopts CUDA language, and can be implemented on any NVIDIA GPU device supporting CUDA architecture. Before implementing the method described in the present invention, the cudaMalloc function should be called to allocate 3 memory areas of the same size on the GPU device side, which are respectively recorded as memory areas A, B, and C. After using the method described in the present invention, the cudaFree function should also be called to release these 3 memory areas. Such as figure 2 As shown, the method of the present invention divides the SAR image into M×M=M 2 Sub-image blocks of the same size are calculated. Due to the influence of the hardware watchdog, the inventive method divides the SRAD filtering process into two kernels (kernel functions) and calculates, and the number of threads (threads) in each block is set to be 16 * 16=256, representing the number of kernels The number of blocks in Grid is:

[0032] ImageWide ...

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Abstract

The invention discloses a method for realizing the acceleration of anisotropic diffusion filtration of an overlarge synthetic aperture radar (SAR) image by a graphic processing unit (GPU), which solves the problem of low speed caused in the process of processing the overlarge SAR image by adopting anisotropic diffusion filtration. An anisotropic diffusion and filtration process adopts a compute unified device architecture (CUDA) and executes the following steps in the GPU: (1) copying image data I from a host memory of a computer to a memory area A of the GPU; (2) computing diffusion dimension data c(q) of the image data I by using an anisotropic diffusion dimension function; (3) computing the graphical data of an anisotropic diffusion filtration result according to an anisotropic diffusion dimension functional equation; and (4) circularly repeating step (2) and step (3) for T times to obtain the final anisotropic diffusion filtration result graphic IT, and copying the data IT in the memory area C to the host memory of the computer after iteration ends. In the invention, the acceleration is accomplished by the GPU parallel computation under the CUDA, compared with the central processing unit (CPU) serial computation, the processing speed is obviously improved, and the method can be applied to sites with high real-time processing requirements.

Description

technical field [0001] The present invention relates to the field of remote sensing image processing, and more specifically to the field of SAR image filtering. It is a method for accelerating the anisotropic diffusion filtering of ultra-large SAR images by using a GPU, and is used to improve the processing speed of image filtering. Background technique [0002] Speckle noise in radar images greatly reduces the readability of images, which is not conducive to image interpretation and information extraction. In this regard, researchers at home and abroad have done a lot of research work. An ideal filtering method should be able to adaptively smooth the speckle noise, keep the sharp change of the edge and feature boundary, and keep the texture information at the same time. [0003] In the past two decades, image processing methods based on partial differential equations (Partial Differential Equations, PDE) have been greatly developed, and its application range covers almost t...

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Application Information

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IPC IPC(8): G06T1/20G06T5/00
Inventor 公茂果焦李成周智强马文萍马晶晶尚荣华王桂婷李阳阳左弟俊付磊曹宇
Owner XIDIAN UNIV
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