  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.
