FourLLIE: Boosting Low-Light Image Enhancement by Fourier Frequency Information

• URL: http://arxiv.org/abs/2308.03033v1
• Date: Sun, 6 Aug 2023 06:14:14 GMT
• Title: FourLLIE: Boosting Low-Light Image Enhancement by Fourier Frequency Information
• Authors: Chenxi Wang, Hongjun Wu, Zhi Jin
• Abstract summary: We propose a two-stage Fourier-based Low-Light Image Enhancement (LLIE) network (FourLLIE) In the first stage, we improve the lightness of low-light images by estimating the amplitude transform map in the Fourier space. In the second stage, we introduce the Signal-to-Noise-Ratio (SNR) map to provide the prior for integrating the global Fourier frequency and the local spatial information.
• Score: 19.478293277978935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, Fourier frequency information has attracted much attention in Low-Light Image Enhancement (LLIE). Some researchers noticed that, in the Fourier space, the lightness degradation mainly exists in the amplitude component and the rest exists in the phase component. By incorporating both the Fourier frequency and the spatial information, these researchers proposed remarkable solutions for LLIE. In this work, we further explore the positive correlation between the magnitude of amplitude and the magnitude of lightness, which can be effectively leveraged to improve the lightness of low-light images in the Fourier space. Moreover, we find that the Fourier transform can extract the global information of the image, and does not introduce massive neural network parameters like Multi-Layer Perceptrons (MLPs) or Transformer. To this end, a two-stage Fourier-based LLIE network (FourLLIE) is proposed. In the first stage, we improve the lightness of low-light images by estimating the amplitude transform map in the Fourier space. In the second stage, we introduce the Signal-to-Noise-Ratio (SNR) map to provide the prior for integrating the global Fourier frequency and the local spatial information, which recovers image details in the spatial space. With this ingenious design, FourLLIE outperforms the existing state-of-the-art (SOTA) LLIE methods on four representative datasets while maintaining good model efficiency.

Related papers

• Wavelet-based Mamba with Fourier Adjustment for Low-light Image Enhancement [26.13172849144202]
  We propose a novel Wavelet-based Mamba with Fourier Adjustment model called WalMaFa. WMB is adopted in the Decoder and FFAB is adopted in the Latent-Decoder structure. Experiments demonstrate that our proposed WalMaFa achieves state-of-the-art performance with fewer computational resources and faster speed.
  arXiv  Detail & Related papers  (2024-10-27T02:48:28Z)
• F2former: When Fractional Fourier Meets Deep Wiener Deconvolution and Selective Frequency Transformer for Image Deblurring [8.296475046681696]
  We propose a novel approach based on the Fractional Fourier Transform (FRFT), a unified spatial-frequency representation. We show that the performance of our proposed method is superior to other state-of-the-art (SOTA) approaches.
  arXiv  Detail & Related papers  (2024-09-03T17:05:12Z)
• FourierMamba: Fourier Learning Integration with State Space Models for Image Deraining [71.46369218331215]
  Image deraining aims to remove rain streaks from rainy images and restore clear backgrounds. We propose a new framework termed FourierMamba, which performs image deraining with Mamba in the Fourier space.
  arXiv  Detail & Related papers  (2024-05-29T18:58:59Z)
• Spatial-frequency Dual-Domain Feature Fusion Network for Low-Light Remote Sensing Image Enhancement [49.15531684596958]
  We propose a Dual-Domain Feature Fusion Network (DFFN) for low-light remote sensing image enhancement. The first phase learns amplitude information to restore image brightness, and the second phase learns phase information to refine details. We have constructed two dark light remote sensing datasets to address the current lack of datasets in dark light remote sensing image enhancement.
  arXiv  Detail & Related papers  (2024-04-26T13:21:31Z)
• Misalignment-Robust Frequency Distribution Loss for Image Transformation [51.0462138717502]
  This paper aims to address a common challenge in deep learning-based image transformation methods, such as image enhancement and super-resolution. We introduce a novel and simple Frequency Distribution Loss (FDL) for computing distribution distance within the frequency domain. Our method is empirically proven effective as a training constraint due to the thoughtful utilization of global information in the frequency domain.
  arXiv  Detail & Related papers  (2024-02-28T09:27:41Z)
• A Dual Domain Multi-exposure Image Fusion Network based on the Spatial-Frequency Integration [57.14745782076976]
  Multi-exposure image fusion aims to generate a single high-dynamic image by integrating images with different exposures. We propose a novelty perspective on multi-exposure image fusion via the Spatial-Frequency Integration Framework, named MEF-SFI. Our method achieves visual-appealing fusion results against state-of-the-art multi-exposure image fusion approaches.
  arXiv  Detail & Related papers  (2023-12-17T04:45:15Z)
• Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement [78.67036949708795]
  Ultra-High-Definition (UHD) photo has gradually become the standard configuration in advanced imaging devices. We propose a new solution, UHDFour, that embeds Fourier transform into a cascaded network. We also contribute the first real UHD LLIE dataset, textbfUHD-LL, that contains 2,150 low-noise/normal-clear 4K image pairs.
  arXiv  Detail & Related papers  (2023-02-23T07:43:41Z)
• Deep Fourier Up-Sampling [100.59885545206744]
  Up-sampling in the Fourier domain is more challenging as it does not follow such a local property. We propose a theoretically sound Deep Fourier Up-Sampling (FourierUp) to solve these issues.
  arXiv  Detail & Related papers  (2022-10-11T06:17:31Z)

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.