计算机应用研究2017，Vol.34Issue(10)：3153-3156,3161,5.DOI:10.3969/j.issn.1001-3695.2017.10.060
结合分块噪声估计的字典学习图像去噪算法
Image denoising algorithm combined with dictionary learning and blocked-based noise estimation
摘要
Abstract
In recent years,the K-SVD dictionary learning denoising algorithm has been widely concerned and applied because of its short time consuming and outstanding performance.But the application of this algorithm requires that the noise in image is additive noise and standard deviation of the noise is known.In view of this situation,this paper proposed a method to select the smooth image blocks and combined it with the singular value decomposition (SVD) to achieve the estimation of the noise standard deviation of the image.Then it proposed a new denoising algorithm which had the characteristic of noise estimation combining with the obtained noise estimation method and the K-SVD dictionary learning denoising algorithm.Experimental results of denoising some images show that,compared with Donoho wavelet soft threshold denoising algorithm and the total variation (TV) denoising algorithm,not only the peak signal to noise ratio(PSNR) of the image denoised by the proposed algorithm is improved by about 1 ～ 3 dB,but also the detailed information and edge features of the image can be better preserved.关键词
图像去噪/平滑图像块/奇异值分解/噪声估计/字典学习Key words
image denoising/smooth image block/singular value decomposition/noise estimation/dictionary learning分类
信息技术与安全科学引用本文复制引用
汪浩然,夏克文,牛文佳,任苗苗,李绰..结合分块噪声估计的字典学习图像去噪算法[J].计算机应用研究,2017,34(10):3153-3156,3161,5.基金项目
国家自然科学基金资助项目(51208168) （51208168）
天津市自然科学基金资助项目(13JCYBJC37700) （13JCYBJC37700）
河北省自然科学基金资助项目(E2016202341) （E2016202341）
大学生创新创业训练计划项目(河北省重点)(201510080051) （河北省重点）
