Con-Net: A Consolidated Light-Weight Image Restoration Network

Abstract

There has been a considerable gap between the recent high-resolution display technologies and the short storage of its content. However, most of the existing restoration methods are restricted by local convolution operations and equal treatment of the diverse information in degraded image. These approaches being degradation-specific employ the same rigid spatial processing across different images ultimately resulting in high memory consumption. For overcoming this limitation we propose Con-Net, a network design capable of exploiting the non-uniformities of the degradations in spatial-domain with limited number of parameters (656k). Our proposed Con-Net comprises of basically two main components, (1) a spatial-degradation aware network for extracting the diverse information inherent in any degraded image, and (2) a holistic attention refinement network for exploiting the knowledge from the degradation aware network to selectively restore the degraded pixels. In a nutshell, our proposed method is generalizable for three applications: image denoising, super-resolution and real-world low-light enhancement. Extensive qualitative and quantitative comparison with prior arts on 8 benchmark datasets demonstrates the efficacy of our proposed Con-Net over existing state-of-the-art degradation-specific architectures, by huge parameter and FLOPs reduction in all the three tasks.