Computer Science ›› 2018, Vol. 45 ›› Issue (7): 264-270.doi: 10.11896/j.issn.1002-137X.2018.07.046

• Graphics, Image & Pattem Recognition • Previous Articles     Next Articles

Dictionary Learning Image Denoising Algorithm Combining Second Generation Bandelet Transform Block

ZHANG Zhen-zhen ,WANG Jian-lin   

  1. College of Computer and Information Engineering,Henan University,Kaifeng,Henan 475000,China
  • Received:2017-05-12 Online:2018-07-30 Published:2018-07-30

Abstract: There are mainly three challenges for sparse coding in the process of image denoising,including incomplete image denoising,the noise residue,and the lack of protection of image edges and detailed characteristics.This paper proposed a dictionary learning image denoising algorithm combining the second generation Bandelet transformation block method to achieve better removal of noise.With the second generation Bandelet transformation,the sparse representation of images can be automatically obtained to accurately estimate the image information according to the regularity of the image geometry manifold.The K-singular value decomposition (K-SVD) algorithm is used to learn the dictionary under the moderate Gaussian white noise variance.Moreover,it utilizes the quadtree segmentation to adaptively predict the noise images and segment images into blocks.Experimental results show that the proposed method can effectively preserve the edge features of image and the fine structure of image while removing the noise.Since it employs the second generation Bandelet transformation for segmentation,the algorithm structure is well optimized and the operational efficiency is also improved.

Key words: Dictionary learning, Image denoising, K-singular value decomposition, Quadtree segmentation, Second generation Bandelet transformation

CLC Number: 

  • TP391.41
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