K-means聚类方法下的复数图像群组稀疏编码降噪算法

稀疏编码已经广泛应用于复数图像的降噪问题，其中，近些年提出的分组稀疏编码由于能够充分利用同一分组图像块的相似性，在滤除噪声和提高降噪信噪比方面具有更大的优势.研究了一种基于K-means聚类方法的复数图像分组稀疏降噪算法，通过改进聚类算法，验证了K-means算法对分组稀疏编码算法的分组有效性.采用在线复数词典训练算法快速获取编码字典，并运用分组正交匹配追踪算法，实现了分组图像块的稀疏编码.通过限制每一分组图像块中编码的相似性，有效抑制了对图像块中噪声的编码，提高了对复数图像的降噪效果.为验证算法的有效性，对模拟和真实的干涉合成孔径雷达图像的仿真噪声进行了定量分析，证明了所提算法相对于以前的分组稀疏编码算法在峰值信噪比指标上有一定的提升.最后对真实的干涉合成孔径雷达图像进行了降噪，进一步验证了所提降噪算法对于真实噪声的降噪能力.

Complex Value Image Group Sparse Coding Denoising Algorithm Based on K-means Clustering Method

Sparse coding has been widely used in complex value image demising. In recent years, the proposed block sparse coding has more advantages in noise filtering and noise reduction because it can make full use of the similarity of patches in the same block. In this paper, a K-means clustering method based sparse demising algorithm for complex image grouping is studied. By improving the clustering algorithm, the grouping effectiveness of K-means algorithm for sparse block coding algorithm is verified. The online complex dictionary training algorithm is used to acquire the coded dictionary quickly, and the sparse coding of block image is realized by using the grouping orthogonal matching pursuit algorithm. By inducing the similarity of the coding in each block, the coding of noise in the block is effectively suppressed and the noise reduction of the complex value image is improved. In order to verify the effectiveness of the proposed algorithm, the demising of simulated and real interferometric synthetic aperture radar images is quantitatively analyzed, which proves that the proposed algorithm has a certain improvement in peak signal-to-noise ratio (PSNR) compared with the previous block sparse coding algorithm. Finally, the real interferometric synthetic aperture radar image is demised, which further verifies the de-noising ability of the proposed algorithm for real noise.