Exploring Fourier Prior and Event Collaboration for Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2508.00308v1
• Date: Fri, 01 Aug 2025 04:25:00 GMT
• Title: Exploring Fourier Prior and Event Collaboration for Low-Light Image Enhancement
• Authors: Chunyan She, Fujun Han, Chengyu Fang, Shukai Duan, Lidan Wang,
• Abstract summary: Event cameras provide performance gain for low-light image enhancement.<n>Currently, existing event-based methods feed a frame and events directly into a single model.<n>We propose a visibility restoration network with amplitude-phase entanglement.<n>In the second stage, a fusion strategy with dynamic alignment is proposed to mitigate the spatial mismatch.
• Score: 1.8724535169356553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The event camera, benefiting from its high dynamic range and low latency, provides performance gain for low-light image enhancement. Unlike frame-based cameras, it records intensity changes with extremely high temporal resolution, capturing sufficient structure information. Currently, existing event-based methods feed a frame and events directly into a single model without fully exploiting modality-specific advantages, which limits their performance. Therefore, by analyzing the role of each sensing modality, the enhancement pipeline is decoupled into two stages: visibility restoration and structure refinement. In the first stage, we design a visibility restoration network with amplitude-phase entanglement by rethinking the relationship between amplitude and phase components in Fourier space. In the second stage, a fusion strategy with dynamic alignment is proposed to mitigate the spatial mismatch caused by the temporal resolution discrepancy between two sensing modalities, aiming to refine the structure information of the image enhanced by the visibility restoration network. In addition, we utilize spatial-frequency interpolation to simulate negative samples with diverse illumination, noise and artifact degradations, thereby developing a contrastive loss that encourages the model to learn discriminative representations. Experiments demonstrate that the proposed method outperforms state-of-the-art models.

Related papers

• Degradation-Consistent Learning via Bidirectional Diffusion for Low-Light Image Enhancement [15.619960776528362]
  Low-light image enhancement aims to improve the visibility of degraded images to better align with human visual perception.<n>We propose a bidirectional diffusion optimization mechanism that jointly models the degradation processes of both low-light and normal-light images.<n>Our method outperforms state-of-the-art methods in both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2025-07-24T07:21:23Z)
• EvRWKV: A RWKV Framework for Effective Event-guided Low-Light Image Enhancement [10.556338127441167]
  EvRWKV is a novel framework that enables continuous cross-modal interaction through dual-domain processing.<n>We show that EvRWKV achieves state-of-the-art performance, effectively enhancing image quality by suppressing noise, restoring structural details, and improving visual clarity in challenging low-light conditions.
  arXiv  Detail & Related papers  (2025-07-01T19:05:04Z)
• Bidirectional Image-Event Guided Low-Light Image Enhancement [18.482432245937247]
  Under extreme low-light conditions, traditional frame-based cameras, due to their limited dynamic range and temporal resolution, face detail loss and motion blur in captured images.<n>To overcome this bottleneck, researchers have introduced event cameras and proposed event-guided low-light image enhancement algorithms.<n>However, these methods neglect the influence of global low-frequency noise caused by dynamic lighting conditions and local structural discontinuities in sparse event data.
  arXiv  Detail & Related papers  (2025-06-06T14:28:17Z)
• InstaRevive: One-Step Image Enhancement via Dynamic Score Matching [66.97989469865828]
  InstaRevive is an image enhancement framework that employs score-based diffusion distillation to harness potent generative capability.<n>Our framework delivers high-quality and visually appealing results across a diverse array of challenging tasks and datasets.
  arXiv  Detail & Related papers  (2025-04-22T01:19:53Z)
• Nonlocal Retinex-Based Variational Model and its Deep Unfolding Twin for Low-Light Image Enhancement [3.174882428337821]
  We propose a variational method for low-light image enhancement based on the Retinex decomposition.<n>A color correction pre-processing step is applied to the low-light image, which is then used as the observed input in the decomposition.<n>We extend the model by introducing its deep unfolding counterpart, in which the operators are replaced with learnable networks.
  arXiv  Detail & Related papers  (2025-04-10T14:48:26Z)
• FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust Fusion [63.87313550399871]
  Image-event joint depth estimation methods leverage complementary modalities for robust perception, yet face challenges in generalizability.<n>We propose Self-supervised Transfer (PST) and FrequencyDe-coupled Fusion module (FreDF)<n>PST establishes cross-modal knowledge transfer through latent space alignment with image foundation models.<n>FreDF explicitly decouples high-frequency edge features from low-frequency structural components, resolving modality-specific frequency mismatches.
  arXiv  Detail & Related papers  (2025-03-25T15:04:53Z)
• Oscillation Inversion: Understand the structure of Large Flow Model through the Lens of Inversion Method [60.88467353578118]
  We show that a fixed-point-inspired iterative approach to invert real-world images does not achieve convergence, instead oscillating between distinct clusters. We introduce a simple and fast distribution transfer technique that facilitates image enhancement, stroke-based recoloring, as well as visual prompt-guided image editing.
  arXiv  Detail & Related papers  (2024-11-17T17:45:37Z)
• Low-Light Video Enhancement via Spatial-Temporal Consistent Decomposition [52.89441679581216]
  Low-Light Video Enhancement (LLVE) seeks to restore dynamic or static scenes plagued by severe invisibility and noise.<n>We present an innovative video decomposition strategy that incorporates view-independent and view-dependent components.<n>Our framework consistently outperforms existing methods, establishing a new SOTA performance.
  arXiv  Detail & Related papers  (2024-05-24T15:56:40Z)
• Zero-Reference Lighting Estimation Diffusion Model for Low-Light Image Enhancement [2.9873893715462185]
  We propose a novel zero-reference lighting estimation diffusion model for low-light image enhancement called Zero-LED.<n>It utilizes the stable convergence ability of diffusion models to bridge the gap between low-light domains and real normal-light domains.<n>It successfully alleviates the dependence on pairwise training data via zero-reference learning.
  arXiv  Detail & Related papers  (2024-03-05T11:39:17Z)
• Mutual Information-driven Triple Interaction Network for Efficient Image Dehazing [54.168567276280505]
  We propose a novel Mutual Information-driven Triple interaction Network (MITNet) for image dehazing. The first stage, named amplitude-guided haze removal, aims to recover the amplitude spectrum of the hazy images for haze removal. The second stage, named phase-guided structure refined, devotes to learning the transformation and refinement of the phase spectrum.
  arXiv  Detail & Related papers  (2023-08-14T08:23:58Z)
• Invertible Network for Unpaired Low-light Image Enhancement [78.33382003460903]
  We propose to leverage the invertible network to enhance low-light image in forward process and degrade the normal-light one inversely with unpaired learning. In addition to the adversarial loss, we design various loss functions to ensure the stability of training and preserve more image details. We present a progressive self-guided enhancement process for low-light images and achieve favorable performance against the SOTAs.
  arXiv  Detail & Related papers  (2021-12-24T17:00:54Z)
• TimeLens: Event-based Video Frame Interpolation [54.28139783383213]
  We introduce Time Lens, a novel indicates equal contribution method that leverages the advantages of both synthesis-based and flow-based approaches. We show an up to 5.21 dB improvement in terms of PSNR over state-of-the-art frame-based and event-based methods.
  arXiv  Detail & Related papers  (2021-06-14T10:33:47Z)

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.