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）无法对受到任意退化影响的低分辨率（LR）图像进行超分辨率重建，因此需要引入退化模型的超分辨率方法。然而，本文揭示了一个现象：仅通过多种模糊核简单训练的非盲超分辨率方法，在盲超分辨率任务中表现出与包含退化模型的方法相当的性能。这一发现促使我们重新审视高性能非盲超分辨率方法，并将其扩展至基于模糊核的盲超分辨率领域。本文提出了两种超分辨率网络，通过迭代端到端方式集成核估计与超分辨率分支。第一个模型称为核条件反投影网络（KCBPN），它通过估计低维核表示来调控超分辨率分支；第二个模型称为核化反投影网络（KBPN），则直接估计原始核并用于图像退化建模。所估计的核不仅用于残差反向传播，还通过前向传播将残差传递至迭代阶段。这种前向传播机制促使各阶段通过聚焦于每阶段中大残差像素来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。本研究代码将开源。