Generative Adversarial Networks for Image Super-Resolution: A Survey

• URL: http://arxiv.org/abs/2204.13620v1
• Date: Thu, 28 Apr 2022 16:35:04 GMT
• Title: Generative Adversarial Networks for Image Super-Resolution: A Survey
• Authors: Chunwei Tian, Xuanyu Zhang, Jerry Chun-Wen Lin, Wangmeng Zuo, Yanning Zhang
• Abstract summary: 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.
• Score: 101.39605080291783
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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. However, there are little literatures summarizing different GANs in SISR. In this paper, we conduct a comparative study of GANs from different perspectives. We first take a look at developments of GANs. Second, we present popular architectures for GANs in big and small samples for image applications. Then, we analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised manners. Next, we compare performance of these popular GANs on public datasets via quantitative and qualitative analysis in SISR. Finally, we highlight challenges of GANs and potential research points for SISR.

Related papers

• 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)
• Memory-augmented Deep Unfolding Network for Guided Image Super-resolution [67.83489239124557]
  Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of a HR image. Previous model-based methods mainly takes the entire image as a whole, and assume the prior distribution between the HR target image and the HR guidance image. We propose a maximal a posterior (MAP) estimation model for GISR with two types of prior on the HR target image.
  arXiv  Detail & Related papers  (2022-02-12T15:37:13Z)
• A Systematic Survey of Deep Learning-based Single-Image Super-Resolution [44.40478296457616]
  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)
  arXiv  Detail & Related papers  (2021-09-29T10:41:41Z)
• Best-Buddy GANs for Highly Detailed Image Super-Resolution [71.13466303340192]
  We consider the single image super-resolution (SISR) problem, where a high-resolution (HR) image is generated based on a low-resolution (LR) input. Most methods along this line rely on a predefined single-LR-single-HR mapping, which is not flexible enough for the SISR task. We propose best-buddy GANs (Beby-GAN) for rich-detail SISR. Relaxing the immutable one-to-one constraint, we allow the estimated patches to dynamically seek the best supervision.
  arXiv  Detail & Related papers  (2021-03-29T02:58:27Z)
• Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies [41.00383742615389]
  Generative Adversarial Networks (GANs) have been extremely successful in various application domains such as computer vision, medicine, and natural language processing. GANs are powerful models for learning complex distributions to synthesize semantically meaningful samples. Given the current fast GANs development, in this survey, we provide a comprehensive review of adversarial models for image synthesis.
  arXiv  Detail & Related papers  (2020-12-26T13:30:42Z)
• Attaining Real-Time Super-Resolution for Microscopic Images Using GAN [0.06345523830122167]
  This paper focuses on improving an existing deep-learning based method to perform Super-Resolution Microscopy in real-time using a standard GPU. We suggest simple changes to the architecture of the generator and the discriminator of SRGAN. We compare the quality and the running time for the outputs produced by our model, opening its applications in different areas like low-end benchtop and even mobile microscopy.
  arXiv  Detail & Related papers  (2020-10-09T15:26:21Z)
• Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution [20.537597542144916]
  We consider a deep cyclic network structure to maintain the domain consistency between the LR and HR data distributions. We propose the Super-Resolution Residual Cyclic Generative Adversarial Network (SRResCycGAN) by training with a generative adversarial network (GAN) framework for the LR to HR domain translation.
  arXiv  Detail & Related papers  (2020-09-07T11:11:18Z)
• A Novel Measure to Evaluate Generative Adversarial Networks Based on Direct Analysis of Generated Images [0.0]
  The Generative Adversarial Network (GAN) is a state-of-the-art technique in the field of deep learning. Here, we consider a fundamental way to evaluate GANs by directly analyzing the images they generate.
  arXiv  Detail & Related papers  (2020-02-27T18:59:22Z)
• Informative Sample Mining Network for Multi-Domain Image-to-Image Translation [101.01649070998532]
  We show that improving the sample selection strategy is an effective solution for image-to-image translation tasks. We propose a novel multi-stage sample training scheme to reduce sample hardness while preserving sample informativeness.
  arXiv  Detail & Related papers  (2020-01-05T05:48:02Z)

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.