Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (11): 168-173.doi: 10.11896/jsjkx.190900004

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Category-specific Image Denoising Algorithm Based on Grid Search

CAO Yi-qin, XIE Shu-hui   

  1. School of Software,East China Jiaotong University,Nanchang 330013,China
  • Received:2019-09-01 Revised:2020-01-06 Online:2020-11-15 Published:2020-11-05
  • About author:CAO Yi-qin,born in 1964,professor,is a member of China Computer Federation.His main research interests include image processing,and pattern recognition.
    XIE Shu-hui,born in 1996,postgra-duate.Her main research interests include image processing,and pattern recognition.
  • Supported by:
    This work was supported by the Key Technology Research and Development Program of the Ministry of Science and Technology of Jiangxi Pro-vince, China (20161BBE50081) and National Natural Science Foundation of China (61663009).

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Abstract: Aiming at the problems of partial region texture loss and time-consuming in similar block search of the category-speci-fic image denoising algorithm,a new denoising algorithm for category-specific image based on grid search is proposed.Firstly,the SSIM is used to select candidate data set similar to the noise image in category-specific data sets.In order to speed up the search of similar blocks,the candidate image set is traversed by a coarse-scale grid search box,and the kNN algorithm is used to find the candidate block in the grid that is close to the noise block.Next,in order to find a candidate block that is closer to the noise block,a fine-scale search box is constructed according to the central position of the candidate block,and the fine-scale search box is traversed to screen the similar block with the closest Euclidean distance between the candidate block and the noise block.Finally,the similar block and the residual component in the regularization of global sparse structure are combined to recover the latent image of the noise image.Experimental results show that the grid search strategy can speed up the selection of similar block,and the residual component can not only remove the image noise,but also better preserve the information at the edge of the image.

Key words: Category-specific image, Global sparse structure regularization, Grid Search, Image denoising, Residual component

CLC Number: 

  • TP391.41
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