基于稀疏表示和梯度先验的图像盲去模糊

针对目前基于稀疏表示的图像盲卷积算法细节恢复有限等问题，提出一种基于稀疏表示和梯度先验的图像盲卷积算法。虽然每个图像块可以通过字典稀疏表示，但是图像块重构出的图像常常出现“伪像”，本文将梯度先验知识和超拉普拉斯先验知识融入稀疏表示盲卷积模型中，采用迭代方法交替估计中间清晰图像和模糊核，一旦获得模糊核，采用超拉普拉斯非盲去卷积算法恢复出最终的清晰图像。实验结果表明，与其他去模糊算法相比，本文算法在抑制振铃方面效果显著。

Blind Image Deblurring with Sparse Representation via Gradient Prior

In view of the limited ability in detail preserving of sparse representation-based blind image convolution algorithm， this paper proposes a blind image convolution algorithm based on sparsity constraints of image patches and gradient prior. Although each image patch can be approximated by a sparse linear combination of atom signals in an over-complete dictionary, it brings about the artificial effects among image blocks. In order to solve this problem, the image gradient prior and Hyper-Laplacian priors are incorporated into the sparse representation blind convolution model to estimate the blur kernel. Once the blur kernel is known, we can apply the non-blind deconvolution algorithm to obtain the latent image, which brings about some ringing effects. Therefore we utilize the Hyper-Laplacian priors to recover the final latent image. Experimental results demonstrate that the proposed improved method can remove artifacts and render better deblurring images, compared to other state-of-the-art deblurring methods.