Previous methods decompose blind super resolution (SR) problem into two sequential steps: \textit{i}) estimating blur kernel from given low-resolution (LR) image and \textit{ii}) restoring SR image based on estimated kernel. This two-step solution involves two independently trained models, which may not well compatible with each other. Small estimation error of the first step could cause severe performance drop of the second one. While on the other hand, the first step can only utilize limited information from LR image, which makes it difficult to predict highly accurate blur kernel. Towards these issues, instead of considering these two steps separately, we adopt an alternating optimization algorithm, which can estimate blur kernel and restore SR image in a single model.

Weaknesses: All weaknesses are related to experiments, analysis and understanding. 1. Missing Methods to compare to: - NTIRE'20 leaders in real-SR tracks seems to be a must. Deep Unfolding Network for Image Super-Resolution CVPR'20 (cited [33] but not compared against) - Cornillere et al. Blind Image Super-Resolution with Spatially Variant Degradations SIGA"19 2. Comparisons settings: Setting2- DIV2KRK is a great choice, but only few methods are tested on it. Also- comparison on non-blind setting with bicubic kernel is important to understand if the improvement is in the upscaling or in the kernel estimation. Using GT kernel and compare, try different intializations, ablate architectural elements (what happens if you do the high-level idea using the basic networks introduced in IKC?- this will let us know if the advantage comes from the elegant idea or from an optimized architecture).

Previous methods decompose blind super resolution (SR) problem into two sequential steps: \textit{i}) estimating blur kernel from given low-resolution (LR) image and \textit{ii}) restoring SR image based on estimated kernel. This two-step solution involves two independently trained models, which may not well compatible with each other. Small estimation error of the first step could cause severe performance drop of the second one. While on the other hand, the first step can only utilize limited information from LR image, which makes it difficult to predict highly accurate blur kernel. Towards these issues, instead of considering these two steps separately, we adopt an alternating optimization algorithm, which can estimate blur kernel and restore SR image in a single model.