Optimization Method of Non-subsampling Contourlet Transform Based on Parallel Computing

A technology of non-subsampling contour and optimization method, applied in the direction of calculation, program code conversion, multi-programming device, etc., can solve the problems of high time overhead and difficult to use efficiently of NSCT algorithm, achieve obvious parallel effect and improve operation speed. , the effect of easy promotion

Active Publication Date: 2021-08-24
SICHUAN UNIV
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Problems solved by technology

Therefore, the excellent performance of NSCT in the field of multi-scale geometric analysis of images has attracted the attention of researchers, but in practical applications, especially for high-resolution images, NSCT needs to perform multiple iterative filtering and two-dimensional convolution , causing the time overhead of the NSCT algorithm to be too large, it is difficult to use it efficiently in practical applications

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  • Optimization Method of Non-subsampling Contourlet Transform Based on Parallel Computing
  • Optimization Method of Non-subsampling Contourlet Transform Based on Parallel Computing
  • Optimization Method of Non-subsampling Contourlet Transform Based on Parallel Computing

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Embodiment Construction

[0044] Below in conjunction with specific embodiment and accompanying drawing, the present invention is further described:

[0045] Such as figure 2 and image 3 Shown a kind of parallel accelerated NSCT method based on CUDA and OpenMP multi-core of the present invention, its specific implementation steps are:

[0046] (1) Convert the matlab source code of NSCT algorithm into C++ code through matlab2cpp tool, using Armadillo library and OpenBLAS library, tool translation;

[0047] (2) The C++ code obtained by using tool translation conversion in step (1) is manually corrected, including the correction of calculation accuracy, etc.;

[0048] (3) According to the GPU and CPU configuration, calculate the number of GPUs that need to be activated for multi-scale decomposition and different-level direction decomposition in the NSCT algorithm, and the actual calculation amount allocated to each GPU;

[0049] Such as Figure 4 and Figure 5 As shown, the step (3) includes the fo...

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Abstract

The invention discloses a non-subsampling contourlet transformation optimization method based on parallel operation, which includes the following steps: (1) According to the configuration of GPU and CPU, calculate and execute the GPU required for multi-scale decomposition and different-level direction decomposition in NSCT algorithm number and the number of CPU threads opened, and the actual calculation amount allocated to each GPU; (2) Parallel analysis of the NSCT decomposition and reconstruction process, it is found that the image data can be moved to the GPU, the convolution is calculated, and the calculation results are returned to the GPU. (3) Use OpenMP and CUDA to execute NSCT decomposition and reconstruction in parallel. The method of the invention can remarkably improve the NSCT operation speed, reduce the running time, and improve the practicability of the NSCT algorithm through parallel execution of processes such as data movement and pixel-level parallel calculation convolution.

Description

technical field [0001] The invention relates to a multi-scale geometric analysis method of an image, in particular to a non-subsampling contourlet transformation optimization method based on parallel operation, which belongs to the technical field of image processing. Background technique [0002] With the extensive research of digital image processing technology, multi-scale geometric analysis and other methods, a variety of transformation methods based on multi-scale geometric analysis have been developed one after another. The most representative transformation is the Contourlet transformation (contourlet transformation). The advantage of this transformation is that its basis functions are distributed in multiple scales and directions. It can effectively capture the edge contour of the image with a small number of transformation coefficients, making up for the Wavelet transform cannot efficiently represent the defects of image edge information. Therefore, Contourlet tran...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/50G06F8/40
CPCG06F8/40G06F9/5027
Inventor 滕奇志张耀王润涵何小海卿粼波熊淑华
Owner SICHUAN UNIV
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