基于跨尺度低秩约束的图像盲解卷积算法EI北大核心CSCD

在模糊核未知的情况下对模糊图像进行复原称为盲解卷积问题,这是一个欠定逆问题,现有的大部分盲解卷积算法利用图像的各种先验知识约束问题的解空间.由于清晰图像的跨尺度自相似性强于模糊图像的跨尺度自相似性,且降采样模糊图像与清晰图像具有更强的相似性,本文提出了一种基于跨尺度低秩约束的单幅图像盲解卷积算法,利用图像跨尺度自相似性,在降采样图像中搜索相似图像块构成相似图像块组,从整体上对相似图像块组进行低秩约束,作为正则项加入到图像盲解卷积的目标函数中,迫使重建图像的边缘接近清晰图像的边缘.本文算法没有对噪声进行特殊处理,由于低秩约束更好地表示了数据的全局结构特性,因此避免了盲解卷积过程受噪声的干扰.在模糊图像和模糊有噪图像上的实验验证了本文的算法能够解决大尺寸模糊核的盲复原并对噪声具有良好的鲁棒性.

Blind Image Deconvolution via Cross-scale Low Rank Prior

Blind image deconvolution aims to recover the sharp image from a blurred one when the blur kernel is unknown. To solve this underdetermined inverse problem, most existing methods exploit various image priors to constrain the solution. Our work is inspired by the observation that the cross-scale self-similarity of the sharp image will diminish after blurring, and the down-sampled blurry image has stronger similarity with the sharp image than the blurry image. In this paper, we propose a blind deconvolution method based on cross-scale low rank prior, in which the similar image patch group is formed from sharper patches sampled from the down-sampled image, and the low rank matrix approximation is used to explore the low rank structure of this group. By introducing the cross-scale low rank prior as the regularization constraint, the intermediate latent image is enforced to contain the sharp edges and fine details. The low rank matrix approximation elegantly indicates the global structure of data, allowing for the noise-avoiding kernel estimation without acquiring any additional handling of noise. Experimental results on blurry images and blurred-noisy images demonstrate that our method can estimate accurate large blur kernels, meanwhile, it has good robustness to noise.