A General Method to Incorporate Spatial Information into Loss Functions for GAN-based Super-resolution Models

• URL: http://arxiv.org/abs/2403.10589v1
• Date: Fri, 15 Mar 2024 17:29:16 GMT
• Title: A General Method to Incorporate Spatial Information into Loss Functions for GAN-based Super-resolution Models
• Authors: Xijun Wang, Santiago López-Tapia, Alice Lucas, Xinyi Wu, Rafael Molina, Aggelos K. Katsaggelos,
• Abstract summary: Generative Adversarial Networks (GANs) have shown great performance on super-resolution problems. GANs often introduce side effects into the outputs, such as unexpected artifacts and noises. We propose a general method that can be effectively used in most GAN-based super-resolution (SR) models by introducing essential spatial information into the training process.
• Score: 25.69505971220203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative Adversarial Networks (GANs) have shown great performance on super-resolution problems since they can generate more visually realistic images and video frames. However, these models often introduce side effects into the outputs, such as unexpected artifacts and noises. To reduce these artifacts and enhance the perceptual quality of the results, in this paper, we propose a general method that can be effectively used in most GAN-based super-resolution (SR) models by introducing essential spatial information into the training process. We extract spatial information from the input data and incorporate it into the training loss, making the corresponding loss a spatially adaptive (SA) one. After that, we utilize it to guide the training process. We will show that the proposed approach is independent of the methods used to extract the spatial information and independent of the SR tasks and models. This method consistently guides the training process towards generating visually pleasing SR images and video frames, substantially mitigating artifacts and noise, ultimately leading to enhanced perceptual quality.

Related papers

• Towards Realistic Data Generation for Real-World Super-Resolution [79.24617577528593]
  RealDGen is an unsupervised learning data generation framework designed for real-world super-resolution. We develop content and degradation extraction strategies, which are integrated into a novel content-degradation decoupled diffusion model. Experiments demonstrate that RealDGen excels in generating large-scale, high-quality paired data that mirrors real-world degradations.
  arXiv  Detail & Related papers  (2024-06-11T13:34:57Z)
• Efficient Visual State Space Model for Image Deblurring [83.57239834238035]
  Convolutional neural networks (CNNs) and Vision Transformers (ViTs) have achieved excellent performance in image restoration. We propose a simple yet effective visual state space model (EVSSM) for image deblurring.
  arXiv  Detail & Related papers  (2024-05-23T09:13:36Z)
• Learning from Multi-Perception Features for Real-Word Image Super-resolution [87.71135803794519]
  We propose a novel SR method called MPF-Net that leverages multiple perceptual features of input images. Our method incorporates a Multi-Perception Feature Extraction (MPFE) module to extract diverse perceptual information. We also introduce a contrastive regularization term (CR) that improves the model's learning capability.
  arXiv  Detail & Related papers  (2023-05-26T07:35:49Z)
• Real-World Image Super-Resolution by Exclusionary Dual-Learning [98.36096041099906]
  Real-world image super-resolution is a practical image restoration problem that aims to obtain high-quality images from in-the-wild input. Deep learning-based methods have achieved promising restoration quality on real-world image super-resolution datasets. We propose Real-World image Super-Resolution by Exclusionary Dual-Learning (RWSR-EDL) to address the feature diversity in perceptual- and L1-based cooperative learning.
  arXiv  Detail & Related papers  (2022-06-06T13:28:15Z)
• Single Image Internal Distribution Measurement Using Non-Local Variational Autoencoder [11.985083962982909]
  This paper proposes a novel image-specific solution, namely non-local variational autoencoder (textttNLVAE) textttNLVAE is introduced as a self-supervised strategy that reconstructs high-resolution images using disentangled information from the non-local neighbourhood. Experimental results from seven benchmark datasets demonstrate the effectiveness of the textttNLVAE model.
  arXiv  Detail & Related papers  (2022-04-02T18:43:55Z)
• Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution [28.00231586840797]
  Single image super-resolution (SISR) with generative adversarial networks (GAN) has recently attracted increasing attention due to its potentials to generate rich details. In this paper, we demonstrate that it is possible to train a GAN-based SISR model which can stably generate perceptually realistic details while inhibiting visual artifacts.
  arXiv  Detail & Related papers  (2022-03-17T09:35:50Z)
• A comparative study of various Deep Learning techniques for spatio-temporal Super-Resolution reconstruction of Forced Isotropic Turbulent flows [0.45935798913942893]
  This study performs super-resolution analysis on turbulent flow fields spatially and temporally using various state-of-the-art machine learning techniques. The dataset used for this study is extracted from the 'isotropic 1024 coarse' dataset which is a part of Johns Hopkins Turbulence databases.
  arXiv  Detail & Related papers  (2021-07-07T17:16:55Z)
• 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)
• Sparse Signal Models for Data Augmentation in Deep Learning ATR [0.8999056386710496]
  We propose a data augmentation approach to incorporate domain knowledge and improve the generalization power of a data-intensive learning algorithm. We exploit the sparsity of the scattering centers in the spatial domain and the smoothly-varying structure of the scattering coefficients in the azimuthal domain to solve the ill-posed problem of over-parametrized model fitting.
  arXiv  Detail & Related papers  (2020-12-16T21:46:33Z)
• Characteristic Regularisation for Super-Resolving Face Images [81.84939112201377]
  Existing facial image super-resolution (SR) methods focus mostly on improving artificially down-sampled low-resolution (LR) imagery. Previous unsupervised domain adaptation (UDA) methods address this issue by training a model using unpaired genuine LR and HR data. This renders the model overstretched with two tasks: consistifying the visual characteristics and enhancing the image resolution. We formulate a method that joins the advantages of conventional SR and UDA models.
  arXiv  Detail & Related papers  (2019-12-30T16:27:24Z)

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.