Training a Better Loss Function for Image Restoration

• URL: http://arxiv.org/abs/2103.14616v1
• Date: Fri, 26 Mar 2021 17:29:57 GMT
• Title: Training a Better Loss Function for Image Restoration
• Authors: Aamir Mustafa, Aliaksei Mikhailiuk, Dan Andrei Iliescu, Varun Babbar and Rafal K. Mantiuk
• Abstract summary: We show that a single natural image is sufficient to train a lightweight feature extractor that outperforms state-of-the-art loss functions in single image super resolution. We propose a novel Multi-Scale Discriminative Feature (MDF) loss comprising a series of discriminators, trained to penalize errors introduced by a generator.
• Score: 17.20936270604533
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Central to the application of neural networks in image restoration problems, such as single image super resolution, is the choice of a loss function that encourages natural and perceptually pleasing results. A popular choice for a loss function is a pre-trained network, such as VGG and LPIPS, which is used as a feature extractor for computing the difference between restored and reference images. However, such an approach has multiple drawbacks: it is computationally expensive, requires regularization and hyper-parameter tuning, and involves a large network trained on an unrelated task. In this work, we explore the question of what makes a good loss function for an image restoration task. First, we observe that a single natural image is sufficient to train a lightweight feature extractor that outperforms state-of-the-art loss functions in single image super resolution, denoising, and JPEG artefact removal. We propose a novel Multi-Scale Discriminative Feature (MDF) loss comprising a series of discriminators, trained to penalize errors introduced by a generator. Second, we show that an effective loss function does not have to be a good predictor of perceived image quality, but instead needs to be specialized in identifying the distortions for a given restoration method.

Related papers

• All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation [47.00239809958627]
  We propose a novel All-in-one Multi-degradation Image Restoration Network (AMIRNet) AMIRNet learns a degradation representation for unknown degraded images by progressively constructing a tree structure through clustering. This tree-structured representation explicitly reflects the consistency and discrepancy of various distortions, providing a specific clue for image restoration.
  arXiv  Detail & Related papers  (2023-08-06T04:51:41Z)
• Restoration of the JPEG Maximum Lossy Compressed Face Images with Hourglass Block based on Early Stopping Discriminator [0.0]
  This paper addresses the restoration of JPEG images that have suffered significant loss due to maximum compression using a GAN-based net-work method. The network incorporates two loss functions, LF Loss and HF Loss, to generate natural and high-performance images. Results show that the blocking phe-nomenon in lost compressed images was removed, and recognizable identities were generated.
  arXiv  Detail & Related papers  (2023-06-22T09:21:48Z)
• Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration [19.561055022474786]
  We study the potential of loss and raise our belief learned loss function empowers the learning capability of neural networks for image and video restoration'' We investigate the efficacy of our belief from three perspectives: 1) from task-customized MAE to native MAE, 2) from image task to video task, and 3) from transformer structure to convolution neural network structure.
  arXiv  Detail & Related papers  (2023-03-29T02:41:08Z)
• Spatially-Adaptive Image Restoration using Distortion-Guided Networks [51.89245800461537]
  We present a learning-based solution for restoring images suffering from spatially-varying degradations. We propose SPAIR, a network design that harnesses distortion-localization information and dynamically adjusts to difficult regions in the image.
  arXiv  Detail & Related papers  (2021-08-19T11:02:25Z)
• Image Restoration by Deep Projected GSURE [115.57142046076164]
  Ill-posed inverse problems appear in many image processing applications, such as deblurring and super-resolution. We propose a new image restoration framework that is based on minimizing a loss function that includes a "projected-version" of the Generalized SteinUnbiased Risk Estimator (GSURE) and parameterization of the latent image by a CNN.
  arXiv  Detail & Related papers  (2021-02-04T08:52:46Z)
• Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
  Blind image restoration is a common yet challenging problem in computer vision. We propose a novel blind image restoration method, aiming to integrate both the advantages of them. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
  arXiv  Detail & Related papers  (2020-08-25T03:30:53Z)
• A Loss Function for Generative Neural Networks Based on Watson's Perceptual Model [14.1081872409308]
  To train Variational Autoencoders (VAEs) to generate realistic imagery requires a loss function that reflects human perception of image similarity. We propose such a loss function based on Watson's perceptual model, which computes a weighted distance in frequency space and accounts for luminance and contrast masking. In experiments, VAEs trained with the new loss function generated realistic, high-quality image samples.
  arXiv  Detail & Related papers  (2020-06-26T15:36:11Z)
• Neural Sparse Representation for Image Restoration [116.72107034624344]
  Inspired by the robustness and efficiency of sparse coding based image restoration models, we investigate the sparsity of neurons in deep networks. Our method structurally enforces sparsity constraints upon hidden neurons. Experiments show that sparse representation is crucial in deep neural networks for multiple image restoration tasks.
  arXiv  Detail & Related papers  (2020-06-08T05:15:17Z)
• Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution [5.258555266148511]
  Single image super-resolution (SISR) is an ill-posed problem with an indeterminate number of valid solutions. We show that the root cause of these pattern artifacts can be traced back to a mismatch between the pre-training objective of perceptual loss and the super-resolved objective.
  arXiv  Detail & Related papers  (2020-05-15T12:36:54Z)
• Invertible Image Rescaling [118.2653765756915]
  We develop an Invertible Rescaling Net (IRN) to produce visually-pleasing low-resolution images. We capture the distribution of the lost information using a latent variable following a specified distribution in the downscaling process.
  arXiv  Detail & Related papers  (2020-05-12T09:55:53Z)
• Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation [181.08127307338654]
  This work presents an effective way to exploit the image prior captured by a generative adversarial network (GAN) trained on large-scale natural images. The deep generative prior (DGP) provides compelling results to restore missing semantics, e.g., color, patch, resolution, of various degraded images.
  arXiv  Detail & Related papers  (2020-03-30T17:45:07Z)

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.