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）无法处理由任意退化模型生成的低分辨率（LR）图像，因此需要结合退化模型的SR方法。然而，本文揭示出：仅使用各种模糊核简单训练的非盲SR，其性能与采用退化模型的盲SR相当。这一发现促使我们重新审视高性能非盲SR，并将其扩展至基于模糊核的盲SR领域。本文提出两种通过核估计与SR分支迭代端到端集成的网络。第一种模型称为核条件反向投影网络（KCBPN），通过估计低维核表示来调控SR分支；第二种模型为核化反向投影网络（KBPN），直接估计原始核并用于建模图像退化过程。估计的核不仅用于反向传播残差，还通过前向传播将残差传递至迭代阶段。这种前向传播机制通过聚焦各阶段大残差像素，促使不同阶段学习多样化的特征。实验结果验证了所提网络在核估计与SR任务中的有效性。我们将公开本研究的代码。