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 ConNet, 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
