Single image super-resolution (SISR), which aims to reconstruct a
high-resolution (HR) image from a low-resolution (LR) observation, has been an
active research topic in the area of image processing in recent decades.
Particularly, deep learning-based super-resolution (SR) approaches have drawn
much attention and have greatly improved the reconstruction performance on
synthetic data. Recent studies show that simulation results on synthetic data
usually overestimate the capacity to super-resolve real-world images. In this
context, more and more researchers devote themselves to develop SR approaches
for realistic images. This article aims to make a comprehensive review on
real-world single image super-resolution (RSISR). More specifically, this
review covers the critical publically available datasets and assessment metrics
for RSISR, and four major categories of RSISR methods, namely the degradation
modeling-based RSISR, image pairs-based RSISR, domain translation-based RSISR,
and self-learning-based RSISR. Comparisons are also made among representative
RSISR methods on benchmark datasets, in terms of both reconstruction quality
and computational efficiency. Besides, we discuss challenges and promising
research topics on RSISR.

通过对公共数据集、评估指标和四类方法的研究以及在基准数据集上的比较，本文全面评估了基于深度学习的单个图像超分辨率（SISR）方法中类别为基于退化建模、基于图像对、基于域转换和基于自学习的 RSISR 方法在重建质量和计算效率方面的表现，并讨论了 RSISR 的挑战和未来研究方向。

实际场景下的单幅图像超分辨率：简要综述

Real-World Single Image Super-Resolution: A Brief Review

Real-world image noise removal is a long-standing yet very challenging task
in computer vision. The success of deep neural network in denoising stimulates
the research of noise generation, aiming at synthesizing more clean-noisy image
pairs to facilitate the training of deep denoisers. In this work, we propose a
novel unified framework to simultaneously deal with the noise removal and noise
generation tasks. Instead of only inferring the posteriori distribution of the
latent clean image conditioned on the observed noisy image in traditional MAP
framework, our proposed method learns the joint distribution of the clean-noisy
image pairs. Specifically, we approximate the joint distribution with two
different factorized forms, which can be formulated as a denoiser mapping the
noisy image to the clean one and a generator mapping the clean image to the
noisy one. The learned joint distribution implicitly contains all the
information between the noisy and clean images, avoiding the necessity of
manually designing the image priors and noise assumptions as traditional.
Besides, the performance of our denoiser can be further improved by augmenting
the original training dataset with the learned generator. Moreover, we propose
two metrics to assess the quality of the generated noisy image, for which, to
the best of our knowledge, such metrics are firstly proposed along this
research line. Extensive experiments have been conducted to demonstrate the
superiority of our method over the state-of-the-arts both in the real noise
removal and generation tasks. The training and testing code is available at
this https URL

本研究提出了一个新的统一框架，旨在同时处理噪声消除和噪声生成任务，学习清洁 - 噪声图像对的联合分布，通过生成器映射干净图像到噪声图像和去噪器映射噪声图像到干净图像以逼近联合分布。本文还提出了两个衡量生成的噪声图像质量的指标，并在实验中证明了该方法在真实噪声消除和生成任务中的优越性。