Blind single image super-resolution (SISR) is a challenging task in image processing due to the ill-posed nature of the inverse problem. Complex degradations present in real life images make it difficult to solve this problem using na\"ive deep learning approaches, where models are often trained on synthetically generated image pairs. Most of the effort so far has been focused on solving the inverse problem under some constraints, such as for a limited space of blur kernels and/or assuming noise-free input images. Yet, there is a gap in the literature to provide a well-generalized deep learning-based solution that performs well on images with unknown and highly complex degradations. In this paper, we propose IKR-Net (Iterative Kernel Reconstruction Network) for blind SISR. In the proposed approach, kernel and noise estimation and high-resolution image reconstruction are carried out iteratively using dedicated deep models. The iterative refinement provides significant improvement in both the reconstructed image and the estimated blur kernel even for noisy inputs. IKR-Net provides a generalized solution that can handle any type of blur and level of noise in the input low-resolution image. IKR-Net achieves state-of-the-art results in blind SISR, especially for noisy images with motion blur.

翻译：盲单图像超分辨率（SISR）是图像处理中的一项挑战性任务，其根源在于逆问题的病态本质。现实图像中存在的复杂退化使得使用朴素深度学习方法难以解决该问题——这些方法中的模型通常是在合成图像对上训练的。现有的大部分研究都集中在某些约束条件下求解逆问题，例如针对有限空间中的模糊核和/或假设无噪声输入图像。然而，现有文献仍缺乏一种泛化性良好的深度学习解决方案，能够在未知且高度复杂的退化图像上取得良好表现。本文提出IKR-Net（迭代核重建网络）用于盲SISR。在该方法中，核与噪声估计以及高分辨率图像重建通过专用深度模型迭代进行。即使对于含噪声输入，这种迭代优化也能显著提升重建图像质量和估计模糊核的精度。IKR-Net提供了一种通用解决方案，可处理输入低分辨率图像中任意类型的模糊和噪声水平。IKR-Net在盲SISR中达到了最先进水平，尤其适用于存在运动模糊的含噪图像。