Integrating Degradation Learning into Image Restoration

Existing image restoration methods usually assume specific degradation model, e.g., linear combination of clean image and degradation map in image denoising and deraining. Benefiting from the power of deep learning, a restoration mapping can be learned from degraded image to latent clean image. In this paper, we propose to integrate degradation learning into image restoration (IDLIR), where degradation model can be learned from training samples. In particular, IDLIR is an iterative restoration framework, where latent clean image and degradation map can be extracted from current residual degraded image, and are then fused by a degradation network to reconstruct degraded image. Then the residual degradation image can be updated by computing the difference between input and reconstructed degraded images. By taking denoising and deraining as examples, IDLIR is compared with state-of-the-art methods on several benchmark datasets. IDLIR performs better than state-of-the-art methods quantitatively and qualitatively.