Single Image Super-Resolution Methods: A Survey

• URL: http://arxiv.org/abs/2202.11763v1
• Date: Thu, 17 Feb 2022 12:01:05 GMT
• Title: Single Image Super-Resolution Methods: A Survey
• Authors: Bahattin Can Maral
• Abstract summary: Super-resolution (SR) is the process of obtaining high-resolution images from one or more low-resolution observations of the same scene. Recently, this popularity has spread into video processing areas to the lengths of developing SR models that work in real-time. In this paper, we compare different SR models that specialize in single image processing and will take a glance at how they evolved to take on many different objectives and shapes over the years.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Super-resolution (SR), the process of obtaining high-resolution images from one or more low-resolution observations of the same scene, has been a very popular topic of research in the last few decades in both signal processing and image processing areas. Due to the recent developments in Convolutional Neural Networks, the popularity of SR algorithms has skyrocketed as the barrier of entry has been lowered significantly. Recently, this popularity has spread into video processing areas to the lengths of developing SR models that work in real-time. In this paper, we compare different SR models that specialize in single image processing and will take a glance at how they evolved to take on many different objectives and shapes over the years.

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)
• SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation [65.88321755969677]
  Deep CNNs have achieved significant successes in image processing and its applications, including single image super-resolution. Recent approaches extend the scope to real-valued upsampling factors. We propose the SRWarp framework to further generalize the SR tasks toward an arbitrary image transformation.
  arXiv  Detail & Related papers  (2021-04-21T02:50:41Z)
• Real-World Super-Resolution of Face-Images from Surveillance Cameras [25.258587196435464]
  We propose a novel framework for generation of realistic LR/HR training pairs. Our framework estimates realistic blur kernels, noise distributions, and JPEG compression artifacts to generate LR images with similar image characteristics as the ones in the source domain. For better perceptual quality we use a Generative Adrial Network (GAN) based SR model where we have exchanged the commonly used VGG-loss [24] with LPIPS-loss [52]
  arXiv  Detail & Related papers  (2021-02-05T11:38:30Z)
• 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 Iterative Residual Convolutional Network for Single Image Super-Resolution [31.934084942626257]
  We propose a deep Iterative Super-Resolution Residual Convolutional Network (ISRResCNet) It exploits the powerful image regularization and large-scale optimization techniques by training the deep network in an iterative manner with a residual learning approach. Our method with a few trainable parameters improves the results for different scaling factors in comparison with the state-of-art methods.
  arXiv  Detail & Related papers  (2020-09-07T12:54:14Z)
• Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS [63.48801313087118]
  We propose a new method for image superresolution using deep residual network with dense skip connections. The proposed method won the first place in all three tracks of the AIM 2020 Real Image Super-Resolution Challenge.
  arXiv  Detail & Related papers  (2020-09-02T22:33:23Z)
• Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
  convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
  arXiv  Detail & Related papers  (2020-03-15T11:04:30Z)
• PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models [77.32079593577821]
  PULSE (Photo Upsampling via Latent Space Exploration) generates high-resolution, realistic images at resolutions previously unseen in the literature. Our method outperforms state-of-the-art methods in perceptual quality at higher resolutions and scale factors than previously possible.
  arXiv  Detail & Related papers  (2020-03-08T16:44:31Z)
• DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-Resolution [69.2432352477966]
  Real image super-resolution(Real-SR) focus on the relationship between real-world high-resolution(HR) and low-resolution(LR) image. In this article, we propose a Dual-path Dynamic Enhancement Network(DDet) for Real-SR. Unlike conventional methods which stack up massive convolutional blocks for feature representation, we introduce a content-aware framework to study non-inherently aligned image pair.
  arXiv  Detail & Related papers  (2020-02-25T18:24:51Z)

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.