IDENet: Implicit Degradation Estimation Network for Efficient Blind Super Resolution

Asif Hussain Khan, Christian Micheloni, Niki Martinel

Abstract: Blind image super-resolution (SR) aims to recover high-resolution (HR) images from low-resolution (LR) inputs hindered by unknown degradation. Existing blind SR methods exploit computationally demanding explicit degradation estimators hinging on the availability of ground-truth information about the degradation process, thus introducing a severe limitation in real-world scenarios where this is inherently unattainable. Implicit degradation estimators avoid the need for ground truth but perform poorly. Our model reduces this performance gap with (i) a novel loss component to implicitly learn the degradation kernel from the LR input only, and (ii) a novel learnable Wiener filter module that exploits the learned degradation kernel to efficiently solve the deconvolution task via a closed-form solution formulated in the Fourier domain. Systematic experiments show that our proposed approach outperforms existing implicit blind SR methods (3dB PSNR gain and 8.5% SSIM improvement on average) and achieves comparable performance to explicit blind SR methods (0.6dB and 0.5% difference in PSNR and SSIM, respectively). Remarkably, these results are obtained using 33% and 71% less parameters than implicit and explicit methods.