Zero-Shot Low-Light Image Enhancement via Joint Frequency Domain Priors Guided Diffusion

• URL: http://arxiv.org/abs/2411.13961v1
• Date: Thu, 21 Nov 2024 09:16:51 GMT
• Title: Zero-Shot Low-Light Image Enhancement via Joint Frequency Domain Priors Guided Diffusion
• Authors: Jinhong He, Shivakumara Palaiahnakote, Aoxiang Ning, Minglong Xue,
• Abstract summary: We will propose a new zero-shot low-light enhancement method to compensate for the lack of light and structural information in the diffusion sampling process. The inspiration comes from the similarity between the wavelet and Fourier frequency domains. Sufficient experiments show that the framework is robust and effective in various scenarios.
• Score: 2.3874115898130865
• License:
• Abstract: Due to the singularity of real-world paired datasets and the complexity of low-light environments, this leads to supervised methods lacking a degree of scene generalisation. Meanwhile, limited by poor lighting and content guidance, existing zero-shot methods cannot handle unknown severe degradation well. To address this problem, we will propose a new zero-shot low-light enhancement method to compensate for the lack of light and structural information in the diffusion sampling process by effectively combining the wavelet and Fourier frequency domains to construct rich a priori information. The key to the inspiration comes from the similarity between the wavelet and Fourier frequency domains: both light and structure information are closely related to specific frequency domain regions, respectively. Therefore, by transferring the diffusion process to the wavelet low-frequency domain and combining the wavelet and Fourier frequency domains by continuously decomposing them in the inverse process, the constructed rich illumination prior is utilised to guide the image generation enhancement process. Sufficient experiments show that the framework is robust and effective in various scenarios. The code will be available at: \href{https://github.com/hejh8/Joint-Wavelet-and-Fourier-priors-guided-diffusion}{https://github.com/hejh8/Joint-Wavelet-and-Fourier-priors-guided-diffusion}.

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)
• 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)
• Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning [81.98675881423131]
  This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images. Existing frequency-based paradigms have relied on frequency-level artifacts introduced during the up-sampling in GAN pipelines to detect forgeries. We introduce a novel frequency-aware approach called FreqNet, centered around frequency domain learning, specifically designed to enhance the generalizability of deepfake detectors.
  arXiv  Detail & Related papers  (2024-03-12T01:28:00Z)
• 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)
• Low-light Image Enhancement via CLIP-Fourier Guided Wavelet Diffusion [28.049668999586583]
  We propose a novel and robust low-light image enhancement method via CLIP-Fourier Guided Wavelet Diffusion, abbreviated as CFWD. CFWD leverages multimodal visual-language information in the frequency domain space created by multiple wavelet transforms to guide the enhancement process. Our approach outperforms existing state-of-the-art methods, achieving significant progress in image quality and noise suppression.
  arXiv  Detail & Related papers  (2024-01-08T10:08:48Z)
• 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)
• FourLLIE: Boosting Low-Light Image Enhancement by Fourier Frequency Information [19.478293277978935]
  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.
  arXiv  Detail & Related papers  (2023-08-06T06:14:14Z)
• WaveFill: A Wavelet-based Generation Network for Image Inpainting [57.012173791320855]
  WaveFill is a wavelet-based inpainting network that decomposes images into multiple frequency bands. WaveFill decomposes images by using discrete wavelet transform (DWT) that preserves spatial information naturally. It applies L1 reconstruction loss to the low-frequency bands and adversarial loss to high-frequency bands, hence effectively mitigate inter-frequency conflicts.
  arXiv  Detail & Related papers  (2021-07-23T04:44:40Z)
• A Fourier-based Framework for Domain Generalization [82.54650565298418]
  Domain generalization aims at tackling this problem by learning transferable knowledge from multiple source domains in order to generalize to unseen target domains. This paper introduces a novel Fourier-based perspective for domain generalization. Experiments on three benchmarks have demonstrated that the proposed method is able to achieve state-of-the-arts performance for domain generalization.
  arXiv  Detail & Related papers  (2021-05-24T06:50:30Z)

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.