摘要
研究了一种匹配梯度分布的组稀疏表示图像去噪模型,将相似图像块构成的结构组作为稀疏表示单元并加入梯度直方图保持正则项匹配梯度分布,基于非精确增广拉格朗日乘子法进行求解得到恢复图像.仿真实验结果表明,该方法不仅可以减少图像处理的时间并可在有效去除噪声的同时保持图像精细纹理结构,获得了较高的峰值信噪比和结构相似性索引测度.在需要得到图像丰富细节的情况下,该方法具有实用价值和现实意义.
A matching gradient distribution group based sparse representation model is researched, in which the basic unit of sparse representation is the group composed by nonlocal patches with similar structures, simultaneously the gradient histogram preserving regularization is added to match gradient distribution, and imprecise Augmented Lagrange multiplier method is used to solve the model. It is well shown by the results that this method can not only shorten the time of image processing, but also retain the fine or small-scale texture structure as well as denoising effectively, and obtain higher output PSNR and SSIM than some current state-of-the-art schemes. In case of the rich details of the image is needed, this method has practical value and realistic significance.
出处
《微电子学与计算机》
CSCD
北大核心
2017年第6期99-103,共5页
Microelectronics & Computer
基金
国家自然科学基金(61203245)
河北省自然科学基金(F2012202027)
关键词
图像去噪
稀疏表示
相似性
纹理
image denoising
sparse representation
similarity
texture
