Since non-blind Super Resolution (SR) fails to super-resolve Low-Resolution (LR) images degraded by arbitrary degradations, SR with the degradation model is required. However, this paper reveals that non-blind SR that is trained simply with various blur kernels exhibits comparable performance as those with the degradation model for blind SR. This result motivates us to revisit high-performance non-blind SR and extend it to blind SR with blur kernels. This paper proposes two SR networks by integrating kernel estimation and SR branches in an iterative end-to-end manner. In the first model, which is called the Kernel Conditioned Back-Projection Network (KCBPN), the low-dimensional kernel representations are estimated for conditioning the SR branch. In our second model, the Kernelized BackProjection Network (KBPN), a raw kernel is estimated and directly employed for modeling the image degradation. The estimated kernel is employed not only for back-propagating its residual but also for forward-propagating the residual to iterative stages. This forward-propagation encourages these stages to learn a variety of different features in different stages by focusing on pixels with large residuals in each stage. Experimental results validate the effectiveness of our proposed networks for kernel estimation and SR. We will release the code for this work.

翻译：由于非盲超分辨率（SR）无法对受任意退化影响的低分辨率（LR）图像进行超分辨率重建，因此需要结合退化模型的超分辨率方法。然而，本文揭示，仅通过不同模糊核训练的非盲超分辨率模型，在与含退化模型的盲超分辨率方法对比时，展现出可比的性能。这一发现促使我们重新审视高性能非盲超分辨率技术，并将其扩展至基于模糊核的盲超分辨率场景。本文提出两种以迭代端到端方式整合核估计与超分辨率分支的神经网络。第一种模型称为核条件反投影网络（KCBPN），通过估计低维核表征来调节超分辨率分支。第二种模型——核化反投影网络（KBPN）——则直接估计原始模糊核并用于建模图像退化过程。该估计核不仅用于残差的反向传播，还通过正向传播机制将残差传递至迭代阶段，促使各阶段聚焦于当前阶段残差较大的像素，从而学习不同阶段的多样化特征。实验结果验证了所提网络在核估计与超分辨率任务中的有效性。本研究将开源相关代码。