论文标题
排名一网：图像修复的有效框架
Rank-One Network: An Effective Framework for Image Restoration
论文作者
论文摘要
图像的主要等级 - 一个（RO）组件表示图像的自相似性，这是图像恢复的重要属性。但是，损坏的图像的RO组件可能会因图像DeNoing的过程而被削弱。我们建议应利用RO特性，并应避免在图像恢复中避免拆卸。为了实现这一目标，我们提出了一个由两个模块组成的新框架，即RO分解和RO重建。 RO分解是为将损坏的图像分解为RO组件和残留的。这是通过将RO预测依次应用于图像或其残差来提取RO组件来实现的。基于神经网络的RO预测提取图像的最接近的RO组件。 RO重建旨在分别从RO组件和残差重建重要信息，并从此重建信息中恢复图像。对四个任务的实验结果，即无噪声图像超分辨率（SR），逼真的图像SR，灰度尺度图像denoising和彩色图像降解，表明该方法可以有效且有效地恢复图像，并且为现实的图像SR和彩色图像降级提供了卓越的性能。
The principal rank-one (RO) components of an image represent the self-similarity of the image, which is an important property for image restoration. However, the RO components of a corrupted image could be decimated by the procedure of image denoising. We suggest that the RO property should be utilized and the decimation should be avoided in image restoration. To achieve this, we propose a new framework comprised of two modules, i.e., the RO decomposition and RO reconstruction. The RO decomposition is developed to decompose a corrupted image into the RO components and residual. This is achieved by successively applying RO projections to the image or its residuals to extract the RO components. The RO projections, based on neural networks, extract the closest RO component of an image. The RO reconstruction is aimed to reconstruct the important information, respectively from the RO components and residual, as well as to restore the image from this reconstructed information. Experimental results on four tasks, i.e., noise-free image super-resolution (SR), realistic image SR, gray-scale image denoising, and color image denoising, show that the method is effective and efficient for image restoration, and it delivers superior performance for realistic image SR and color image denoising.
