Wavelet-based Mamba with Fourier Adjustment for Low-light Image Enhancement

• URL: http://arxiv.org/abs/2410.20314v1
• Date: Sun, 27 Oct 2024 02:48:28 GMT
• Title: Wavelet-based Mamba with Fourier Adjustment for Low-light Image Enhancement
• Authors: Junhao Tan, Songwen Pei, Wei Qin, Bo Fu, Ximing Li, Libo Huang,
• Abstract summary: 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.
• Score: 26.13172849144202
• License:
• Abstract: Frequency information (e.g., Discrete Wavelet Transform and Fast Fourier Transform) has been widely applied to solve the issue of Low-Light Image Enhancement (LLIE). However, existing frequency-based models primarily operate in the simple wavelet or Fourier space of images, which lacks utilization of valid global and local information in each space. We found that wavelet frequency information is more sensitive to global brightness due to its low-frequency component while Fourier frequency information is more sensitive to local details due to its phase component. In order to achieve superior preliminary brightness enhancement by optimally integrating spatial channel information with low-frequency components in the wavelet transform, we introduce channel-wise Mamba, which compensates for the long-range dependencies of CNNs and has lower complexity compared to Diffusion and Transformer models. So in this work, we propose a novel Wavelet-based Mamba with Fourier Adjustment model called WalMaFa, consisting of a Wavelet-based Mamba Block (WMB) and a Fast Fourier Adjustment Block (FFAB). We employ an Encoder-Latent-Decoder structure to accomplish the end-to-end transformation. Specifically, WMB is adopted in the Encoder and Decoder to enhance global brightness while FFAB is adopted in the Latent to fine-tune local texture details and alleviate ambiguity. Extensive experiments demonstrate that our proposed WalMaFa achieves state-of-the-art performance with fewer computational resources and faster speed. Code is now available at: https://github.com/mcpaulgeorge/WalMaFa.

Related papers

• Zero-Shot Low-Light Image Enhancement via Joint Frequency Domain Priors Guided Diffusion [2.3874115898130865]
  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.
  arXiv  Detail & Related papers  (2024-11-21T09:16:51Z)
• DiMSUM: Diffusion Mamba -- A Scalable and Unified Spatial-Frequency Method for Image Generation [4.391439322050918]
  We introduce a novel state-space architecture for diffusion models. We harness spatial and frequency information to enhance the inductive bias towards local features in input images.
  arXiv  Detail & Related papers  (2024-11-06T18:59:17Z)
• 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)
• WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting [61.64303388738395]
  We propose a Wavelet-Fourier Transform Network (WFTNet) for long-term time series forecasting. Tests on various time series datasets show WFTNet consistently outperforms other state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-09-20T13:44:18Z)
• 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)
• QFF: Quantized Fourier Features for Neural Field Representations [28.82293263445964]
  We show that using Quantized Fourier Features (QFF) can result in smaller model size, faster training, and better quality outputs for several applications. QFF are easy to code, fast to compute, and serve as a simple drop-in addition to many neural field representations.
  arXiv  Detail & Related papers  (2022-12-02T00:11:22Z)
• Inception Transformer [151.939077819196]
  Inception Transformer, or iFormer, learns comprehensive features with both high- and low-frequency information in visual data. We benchmark the iFormer on a series of vision tasks, and showcase that it achieves impressive performance on image classification, COCO detection and ADE20K segmentation.
  arXiv  Detail & Related papers  (2022-05-25T17:59:54Z)
• Frequency-bin entanglement from domain-engineered down-conversion [101.18253437732933]
  We present a single-pass source of discrete frequency-bin entanglement which does not use filtering or a resonant cavity. We use a domain-engineered nonlinear crystal to generate an eight-mode frequency-bin entangled source at telecommunication wavelengths.
  arXiv  Detail & Related papers  (2022-01-18T19:00:29Z)

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.