计算机工程与应用2017，Vol.53Issue(16)：187-192,6.DOI:10.3778/j.issn.1002-8331.1610-0234
基于块分类和字典优化的K-SVD图像去噪研究
K-SVD image denoising based on noisy image blocks classification and dictionary opti-mization.
摘要
Abstract
K-Singular Value Decomposition(K-SVD)algorithm is often used for image denoising by creating an over-complete dictionary for sparse representation. K-SVD algorithm is effective and can keep the original image information as well. However, the image structure is often ignored. Furthermore, noise atoms are still existed in the trained dictionary obtained by K-SVD algorithm, which will result in the poor denoising performance. According to these limitations, a new denoising algorithm is proposed in this paper. First, a more targeted dictionary is obtained by the classification of noisy image blocks. Second, the dictionary atoms are classified into the noise and noiselesscategories, and then the optimized dictionary will be achieved by replacing the noise atoms by overcomplete discrete cosine transform dictionary atoms. Third, the image is denoised using the optimized dictionary. Simulation studies show that in comparison with the curvelet-based denoising method, the non-local mean denoising method and the classical K-SVD denoising method, the new approach has better denoising ability.关键词
图像去噪/稀疏表示/K-SVD算法/图像块分类/过完备字典/字典优化Key words
image denoising/sparse representation/K-Singular Value Decomposition(K-SVD)algorithm/image blocks classification/overcomplete dictionary/dictionary optimization分类
信息技术与安全科学引用本文复制引用
华志胜,付丽华..基于块分类和字典优化的K-SVD图像去噪研究[J].计算机工程与应用,2017,53(16):187-192,6.基金项目
教育部新世纪优秀人才支持计划(No.NCET-13-1011) （No.NCET-13-1011）
湖北省自然科学基金(No.2015CFB555) （No.2015CFB555）
华中师范大学中央高校基本科研业务费教育科学专项(No.230-20205160288) （No.230-20205160288）
中央高校科研业务费(No.CCNU15A05022). （No.CCNU15A05022）
