Single-image super-resolution with joint-optimization of TV regularization and sparse representation

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• 作者：Jinzheng Lu ; ^{lujinzheng@163.com" class="auth_mail} ; Bin Wu
• 关键词：Regularization restoration ; Total variation ; Sparse representation ; Over-complete dictionary ; Image super-resolution
• 刊名：Optik - International Journal for Light and Electron Optics
• 出版年：June 2014
• 年：2014
• 卷：125
• 期：11
• 页码：2497-2504
• 全文大小：1835 K

文摘

A super-resolution (SR) reconstruction framework is proposed using regularization restoration combined with learning-based resolution enhancement via sparse representation. With the viewpoint of conventional learning methods, the original image can be split into low frequency (LF) and high frequency (HF) components. The reconstruction mainly focuses on the process of HF part, while the LF one is founded simply by typical interpolation function. For the severely blurred single-image, we first use regularization restoration technology to recover it. Then the regularized output remarkably betters the quality of LF used in traditional learning-based methods. Last, image resolution enhancement with characteristic of edge preserving can implement based on the acquired relatively sharp intermediate image and the pre-constructed over-complete dictionary for sparse representation. Specifically, the regularization can favorably weaken the dependence of atoms on the course of degradation. With both techniques, we can noticeably eliminate the blur and the edge artifacts in the enlarged image simultaneously. Various experimental results demonstrate that the proposed approach can produce visually pleasing resolution for severely blurred image.