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.

本研究提出了一种名为 IKR-Net (迭代核重构网络) 的方法，通过使用专门的深度模型进行迭代的核和噪声估计以及高分辨率图像重建，提供了一种通用解决方法，能够处理输入低分辨率图像中任意类型的模糊和噪声，并在盲目单图像超分辨率中取得了最先进的结果，特别是对于带有运动模糊的噪声图像。

基于深度学习的盲图像超分辨率：迭代核重建和噪声估计

Deep learning-based blind image super-resolution with iterative kernel  reconstruction and noise estimation

In the blind single image super-resolution (SISR) task, existing works have
been successful in restoring image-level unknown degradations. However, when a
single video frame becomes the input, these works usually fail to address
degradations caused by video compression, such as mosquito noise, ringing,
blockiness, and staircase noise. In this work, we for the first time, present a
video compression-based degradation model to synthesize low-resolution image
data in the blind SISR task. Our proposed image synthesizing method is widely
applicable to existing image datasets, so that a single degraded image can
contain distortions caused by the lossy video compression algorithms. This
overcomes the leak of feature diversity in video data and thus retains the
training efficiency. By introducing video coding artifacts to SISR degradation
models, neural networks can super-resolve images with the ability to restore
video compression degradations, and achieve better results on restoring generic
distortions caused by image compression as well. Our proposed approach achieves
superior performance in SOTA no-reference Image Quality Assessment, and shows
better visual quality on various datasets. In addition, we evaluate the SISR
neural network trained with our degradation model on video super-resolution
(VSR) datasets. Compared to architectures specifically designed for the VSR
purpose, our method exhibits similar or better performance, evidencing that the
presented strategy on infusing video-based degradation is generalizable to
address more complicated compression artifacts even without temporal cues.

通过引入视频压缩降级模型，提出了一种盲目单图像超分辨率方法，可恢复由视频压缩引起的失真，通过合成低分辨率图像数据并训练神经网络，在图像质量评估和视觉质量方面取得了优秀的性能。