A Systematic Survey of Deep Learning-based Single-Image Super-Resolution
- URL: http://arxiv.org/abs/2109.14335v2
- Date: Fri, 12 Apr 2024 08:37:47 GMT
- Title: A Systematic Survey of Deep Learning-based Single-Image Super-Resolution
- Authors: Juncheng Li, Zehua Pei, Wenjie Li, Guangwei Gao, Longguang Wang, Yingqian Wang, Tieyong Zeng,
- Abstract summary: Single-image super-resolution (SISR) is an important task in image processing, which aims to enhance the resolution of imaging systems.
Recently, SISR has made a huge leap and has achieved promising results with the help of deep learning (DL)
- Score: 44.40478296457616
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Single-image super-resolution (SISR) is an important task in image processing, which aims to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and has achieved promising results with the help of deep learning (DL). In this survey, we give an overview of DL-based SISR methods and group them according to their design targets. Specifically, we first introduce the problem definition, research background, and the significance of SISR. Secondly, we introduce some related works, including benchmark datasets, upsampling methods, optimization objectives, and image quality assessment methods. Thirdly, we provide a detailed investigation of SISR and give some domain-specific applications of it. Fourthly, we present the reconstruction results of some classic SISR methods to intuitively know their performance. Finally, we discuss some issues that still exist in SISR and summarize some new trends and future directions. This is an exhaustive survey of SISR, which can help researchers better understand SISR and inspire more exciting research in this field. An investigation project for SISR is provided at https://github.com/CV-JunchengLi/SISR-Survey.
Related papers
- UnmixingSR: Material-aware Network with Unsupervised Unmixing as Auxiliary Task for Hyperspectral Image Super-resolution [5.167168688234238]
This paper proposes a component-aware hyperspectral image (HIS) super-resolution network called UnmixingSR.
We use the bond between LR abundances and HR abundances to boost the stability of our method in solving SR problems.
Experimental results show that unmixing process as an auxiliary task incorporated into the SR problem is feasible and rational.
arXiv Detail & Related papers (2024-07-09T03:41:02Z) - Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss [47.36902705025445]
Super-Resolution for Image Recognition (SR4IR) guides the generation of SR images beneficial to image recognition performance.
In this paper, we demonstrate that our SR4IR achieves outstanding task performance by generating SR images useful for a specific image recognition task.
arXiv Detail & Related papers (2024-04-02T06:52:31Z) - Exploiting Self-Supervised Constraints in Image Super-Resolution [72.35265021054471]
This paper introduces a novel self-supervised constraint for single image super-resolution, termed SSC-SR.
SSC-SR uniquely addresses the divergence in image complexity by employing a dual asymmetric paradigm and a target model updated via exponential moving average to enhance stability.
Empirical evaluations reveal that our SSC-SR framework delivers substantial enhancements on a variety of benchmark datasets, achieving an average increase of 0.1 dB over EDSR and 0.06 dB over SwinIR.
arXiv Detail & Related papers (2024-03-30T06:18:50Z) - Guided Depth Map Super-resolution: A Survey [88.54731860957804]
Guided depth map super-resolution (GDSR) aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image.
A myriad of novel and effective approaches have been proposed recently, especially with powerful deep learning techniques.
This survey is an effort to present a comprehensive survey of recent progress in GDSR.
arXiv Detail & Related papers (2023-02-19T15:43:54Z) - Hierarchical Similarity Learning for Aliasing Suppression Image
Super-Resolution [64.15915577164894]
A hierarchical image super-resolution network (HSRNet) is proposed to suppress the influence of aliasing.
HSRNet achieves better quantitative and visual performance than other works, and remits the aliasing more effectively.
arXiv Detail & Related papers (2022-06-07T14:55:32Z) - Generative Adversarial Networks for Image Super-Resolution: A Survey [101.39605080291783]
Single image super-resolution (SISR) has played an important role in the field of image processing.
Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images with small samples.
In this paper, we conduct a comparative study of GANs from different perspectives.
arXiv Detail & Related papers (2022-04-28T16:35:04Z) - Real-World Single Image Super-Resolution: A Brief Review [44.14123585227239]
Single image super-resolution (SISR) aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) observation.
Deep learning-based super-resolution approaches have drawn much attention and have greatly improved the reconstruction performance on synthetic data.
arXiv Detail & Related papers (2021-03-03T12:41:44Z) - A Comprehensive Review of Deep Learning-based Single Image
Super-resolution [4.234711903716694]
This survey is an effort to provide a detailed survey of recent progress in the field of super-resolution in the perspective of deep learning.
The survey classifies the image SR methods into four categories, i.e., classical methods, supervised learning-based methods, unsupervised learning-based methods, and domain-specific SR methods.
Deep learning-based approaches of SR are evaluated using a reference dataset.
arXiv Detail & Related papers (2021-02-18T14:04:25Z) - Video Super Resolution Based on Deep Learning: A Comprehensive Survey [87.30395002197344]
We comprehensively investigate 33 state-of-the-art video super-resolution (VSR) methods based on deep learning.
We propose a taxonomy and classify the methods into six sub-categories according to the ways of utilizing inter-frame information.
We summarize and compare the performance of the representative VSR method on some benchmark datasets.
arXiv Detail & Related papers (2020-07-25T13:39:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.