Convolutional Neural Network (CNN)-based image super-resolution (SR) has exhibited impressive success on known degraded low-resolution (LR) images. However, this type of approach is hard to hold its performance in practical scenarios when the degradation process is unknown. Despite existing blind SR methods proposed to solve this problem using blur kernel estimation, the perceptual quality and reconstruction accuracy are still unsatisfactory. In this paper, we analyze the degradation of a high-resolution (HR) image from image intrinsic components according to a degradation-based formulation model. We propose a components decomposition and co-optimization network (CDCN) for blind SR. Firstly, CDCN decomposes the input LR image into structure and detail components in feature space. Then, the mutual collaboration block (MCB) is presented to exploit the relationship between both two components. In this way, the detail component can provide informative features to enrich the structural context and the structure component can carry structural context for better detail revealing via a mutual complementary manner. After that, we present a degradation-driven learning strategy to jointly supervise the HR image detail and structure restoration process. Finally, a multi-scale fusion module followed by an upsampling layer is designed to fuse the structure and detail features and perform SR reconstruction. Empowered by such degradation-based components decomposition, collaboration, and mutual optimization, we can bridge the correlation between component learning and degradation modelling for blind SR, thereby producing SR results with more accurate textures. Extensive experiments on both synthetic SR datasets and real-world images show that the proposed method achieves the state-of-the-art performance compared to existing methods.
翻译:在已知的低分辨率(LR)图像上,这种类型的方法在已知的低分辨率(LR)图像上取得了令人印象深刻的成功。然而,当降解过程未知时,很难在实际情景中保持其性能。尽管现有的盲目SR方法建议使用模糊的内核估计来解决这一问题,但概念质量和重建准确性仍然不能令人满意。在本文件中,我们分析高分辨率(HR)图像根据基于退化的配方模型从图像内在组成部分中退化的情况。我们为盲方SR提出一个分解和共同优化网络(CDCN)。首先,CDCN将输入的LR图像在特性空间的结构和细节部分中进行分解。然后,提出共同合作区(MCB)来利用这两个组成部分之间的关系。这样,细节部分可以提供信息性特征来丰富结构环境,而结构组成部分可以包含结构性背景,通过相互互补的方式进行更详尽的显示由州驱动的学习战略,共同监督HR的文字细节和结构结构恢复过程之间的分解。最后,一个多尺度的模块可以显示我们为进行这种结构的升级和结构的升级结构,然后是模拟结构结构进行我们为结构的升级的升级的升级结构,然后进行。