1. 基于自适应平方变换的工件去噪方法.
- Author
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刘秀平, 薛婷婷, 徐 健, 张凯兵, and 杜勇辰
- Abstract
In wiew of the problem that noise affects workpiece image segmentation and tracking, in this paper, an approach was proposed to address the problem with adaptive square transform. Firstly, the block of noise images was extracted from the noise image and the mean value of the block was subtracted. Then learning square transform with sparse level was fixed. Updating square transform level was done asthe sparse level of the next learning square transform. Finally iteratived learning square transform and sparse level update, and averaged at the denoising blocks’ locations in the image generate denoised image estimate. The experimental results show that the proposed method has better denoising performance. Compared with the K-SVD algorithm, the peak signal-to-noise ratio of the denoised image is about twice that of the K-SVD algorithm, and the denoising speed is 3.9 times that of the K-SVD algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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