关键词: 联合稀疏变换学习, 光源, 工件图像, 去噪块

Abstract: As the quality of light in visual measurement will directly affect the accuracy and efficiency of workpiece detection, to address the problem, an approach is proposed to denoise the workpiece image under different light sources with union-of-transforms learning. The proposed method firstly extracts the noise image patches from noise images, and studies the intra-cluster transform learning of the noise patches by alternated between sparse coding and transform update steps. Next, the transform sparse levels of clustering signal in each noise patch are calculated, and the minimum sparse level is selected as the sparse level of the denoising patches. Finally, denoised patches are clustered and the denoised image is estimated by the mean value of the final iterative denoised patches. The experimental results show that the proposed mothed is superior to the other algorithms in terms of both denoising effect and algorithm speed for workpiece images under different light sources, and gains better denoise performance.

Key words: union-of-transforms learning, light sources, workpieces pictures, denoising patches