基于稀疏表示和结构自相似性的单幅图像盲解卷积算法

图像盲解卷积研究当模糊核未知时，如何从模糊图像复原出原始清晰图像.由于盲解卷积是一个欠定问题，现有的盲解卷积算法都直接或间接地利用各种先验知识.本文提出了一种结合稀疏表示与结构自相似性的单幅图像盲解卷积算法，该算法将图像的稀疏性先验和结构自相似性先验作为正则化约束加入到图像盲解卷积的目标函数中，并利用图像不同尺度间的结构自相似性，将观测模糊图像的降采样图像作为稀疏表示字典的训练样本，保证清晰图像在该字典下的稀疏性.最后利用交替求解的方式估计模糊核和清晰图像.模拟和真实数据上的实验表明本文算法能够准确估计模糊核，复原清晰的图像边缘，并具有很好的鲁棒性.

Single Image Blind Deconvolution Using Sparse Representation and Structural Self-similarity

Blind image deconvolution aims to recover the latent sharp image from a blurry image when the blur kernel is unknown. Since blind deconvolution is an underdetermined problem, existing methods take advantage of various prior knowledge directly or indirectly. In this article, we propose a single image blind deconvolution method based on sparse representation and structural self-similarity. In our method, we add the image sparsity prior and structural self-similarity prior to the blind deconvolution objective function as regularization constraints, and we utilize the structural self-similarity between different image scales by taking the down-sampled version of observed blurry image as the sparse representation dictionary training set so that the sparsity of the latent sharp image under this dictionary can be ensured. Finally, we estimate the blur kernel and sharp image alternately. Experimental results on both simulated and real blurry images demonstrate that the blur kernels estimated by our method are accurate and robust, and that the restored images have high visual quality with sharp edges.