Image super-resolution reconstruction based on residual compensation combined attention network

Abstract

: For image reconstruction, the residual network ignores part of the residual information when extracting features. We propose an image super-resolution reconstruction based on residual compensation joint attention network (RCCN). Firstly, we construct a three-way residual network for compensating the feature information of the standard residual network; secondly, we design a joint attention module to complement the pixel-level image attention information by 3D attention while the channel attention learns the channel weight information; finally, our method has clearer results compared with other advanced methods, and the objective evaluation indexes are all greatly improved.