摘要： 针对现有去噪算法不能有效去除高斯-椒盐混合噪声的问题,提出一种基于自适应中值滤波与改进稀疏表示的混合去噪算法。采用自适应中值滤波对带噪图像进行初始化,检测并初步抑制脉冲椒盐噪声,利用改进的K奇异值分解字典学习方法与基于回溯自适应的正交匹配追踪稀疏编码方法对处理后的图像进行高斯去噪。实验结果表明,与稀疏非局部正则化加权编码混合去噪算法相比,该算法在混合噪声较大的情况下,具有更高的峰值信噪比和更快的去噪速度。

关键词: 自适应中值滤波, 稀疏表示, 高斯噪声, 椒盐噪声, 混合去噪

Abstract: As current algorithm cannot effectively remove Gaussian and salt-pepper hybrid noise,this paper proposes a hybrid denosing algorithm combining Adaptive Median Filtering(AMF) with improved sparse representation.It uses the AMF to realize initialization for noisy image detect and suppress salt and pepper noise,and then removes Gaussian noise by means of improved K-Singular Value Decomposition(K-SVD) dictionary learning method and Backtracking-based Adaptive Orthogonal Matching Pursuit(BAOMP) sparse coding method.Experimental result shows the proposed algorithm gains higher Peak Signal to Noise Ratio(PSNR) and faster denoising speed than Weighted Encoding with Sparse Nonlocal Regularization(WESNR) algorithm in larger Gaussian noise condition.

Key words: Adaptive Median Filtering(AMF), sparse representation, Gaussian noise, salt and pepper noise, hybrid denosing

中图分类号: 

• TP391