BiEvLight: Bi-level Learning of Task-Aware Event Refinement for Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2603.04975v1
• Date: Thu, 05 Mar 2026 09:14:10 GMT
• Title: BiEvLight: Bi-level Learning of Task-Aware Event Refinement for Low-Light Image Enhancement
• Authors: Zishu Yao, Xiang-Xiang Su, Shengning Zhou, Guang-Yong Chen, Guodong Fan, Xing Chen,
• Abstract summary: Event cameras show great promise for Low-light Image Enhancement (LLIE)<n>Key challenge is the dual degradation from intrinsic background activity (BA) noise in events and low signal-to-noise ratio (SNR) in images.<n>We propose BiEvLight, a hierarchical and task-aware framework that collaboratively optimize enhancement and denoising.
• Score: 24.179374236283408
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Event cameras, with their high dynamic range, show great promise for Low-light Image Enhancement (LLIE). Existing works primarily focus on designing effective modal fusion strategies. However, a key challenge is the dual degradation from intrinsic background activity (BA) noise in events and low signal-to-noise ratio (SNR) in images, which causes severe noise coupling during modal fusion, creating a critical performance bottleneck. We therefore posit that precise event denoising is the prerequisite to unlocking the full potential of event-based fusion. To this end, we propose BiEvLight, a hierarchical and task-aware framework that collaboratively optimizes enhancement and denoising by exploiting their intrinsic interdependence. Specifically, BiEvLight exploits the strong gradient correlation between images and events to build a gradient-guided event denoising prior that alleviates insufficient denoising in heavily noisy regions. Moreover, instead of treating event denoising as a static pre-processing stage-which inevitably incurs a trade-off between over- and under-denoising and cannot adapt to the requirements of a specific enhancement objective-we recast it as a bilevel optimization problem constrained by the enhancement task. Through cross-task interaction, the upper-level denoising problem learns event representations tailored to the lower-level enhancement objective, thereby substantially improving overall enhancement quality. Extensive experiments on the Real-world noise Dataset SDE demonstrate that our method significantly outperforms state-of-the-art (SOTA) approaches, with average improvements of 1.30dB in PSNR, 2.03dB in PSNR* and 0.047 in SSIM, respectively. The code will be publicly available at https://github.com/iijjlk/BiEvlight.

Related papers

• EvRWKV: A Continuous Interactive RWKV Framework for Effective Event-Guided Low-Light Image Enhancement [10.556338127441167]
  Event cameras offer high dynamic range and microsecond temporal resolution by asynchronously capturing brightness changes.<n>We propose EvRWKV, 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 Fusion Framework for Low-Light Image Enhancement [24.5584423318892]
  Under extreme low-light conditions, frame-based cameras suffer from severe detail loss due to limited dynamic range.<n>Recent studies have introduced event cameras for event-guided low-light image enhancement.<n>We propose BiLIE, a Bidirectional image-event guided fusion framework for Low-Light Image Enhancement.
  arXiv  Detail & Related papers  (2025-06-06T14:28:17Z)
• Zero-Shot Image Denoising for High-Resolution Electron Microscopy [28.34992348748098]
  High-resolution electron microscopy (HREM) imaging technique is a powerful tool for directly visualizing a broad range of materials in real-space. It faces challenges in denoising due to ultra-low signal-to-noise ratio (SNR) and scarce data availability. We propose Noise2SR, a zero-shot self-supervised learning (ZS-SSL) denoising framework for HREM.
  arXiv  Detail & Related papers  (2024-06-20T12:40:18Z)
• LED: A Large-scale Real-world Paired Dataset for Event Camera Denoising [19.51468512911655]
  Event camera has significant advantages in capturing dynamic scene information while being prone to noise interference. We construct a new paired real-world event denoising dataset (LED), including 3K sequences with 18K seconds of high-resolution (1200*680) event streams. We propose a novel effective denoising framework(DED) using homogeneous dual events to generate the GT with better separating noise from the raw.
  arXiv  Detail & Related papers  (2024-05-30T06:02:35Z)
• Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling [56.506240377714754]
  We present a novel strategy called the Diffusion Model for Image Denoising (DMID) Our strategy includes an adaptive embedding method that embeds the noisy image into a pre-trained unconditional diffusion model. Our DMID strategy achieves state-of-the-art performance on both distortion-based and perception-based metrics.
  arXiv  Detail & Related papers  (2023-07-08T14:59:41Z)
• Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-Regulation [55.07472635587852]
  Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast. These approaches encounter persistent challenges in efficiently mitigating dynamic noise and accommodating diverse low-light scenarios. We first propose a method for estimating the noise level in low light images in a quick and accurate way. We then devise a Learnable Illumination Interpolator (LII) to satisfy general constraints between illumination and input.
  arXiv  Detail & Related papers  (2023-05-17T13:56:48Z)
• Zero-shot Blind Image Denoising via Implicit Neural Representations [77.79032012459243]
  We propose an alternative denoising strategy that leverages the architectural inductive bias of implicit neural representations (INRs) We show that our method outperforms existing zero-shot denoising methods under an extensive set of low-noise or real-noise scenarios.
  arXiv  Detail & Related papers  (2022-04-05T12:46:36Z)
• IDR: Self-Supervised Image Denoising via Iterative Data Refinement [66.5510583957863]
  We present a practical unsupervised image denoising method to achieve state-of-the-art denoising performance. Our method only requires single noisy images and a noise model, which is easily accessible in practical raw image denoising. To evaluate raw image denoising performance in real-world applications, we build a high-quality raw image dataset SenseNoise-500 that contains 500 real-life scenes.
  arXiv  Detail & Related papers  (2021-11-29T07:22:53Z)
• CERL: A Unified Optimization Framework for Light Enhancement with Realistic Noise [81.47026986488638]
  Low-light images captured in the real world are inevitably corrupted by sensor noise. Existing light enhancement methods either overlook the important impact of real-world noise during enhancement, or treat noise removal as a separate pre- or post-processing step. We present Coordinated Enhancement for Real-world Low-light Noisy Images (CERL), that seamlessly integrates light enhancement and noise suppression parts into a unified and physics-grounded framework.
  arXiv  Detail & Related papers  (2021-08-01T15:31:15Z)
• Progressive Joint Low-light Enhancement and Noise Removal for Raw Images [10.778200442212334]
  Low-light imaging on mobile devices is typically challenging due to insufficient incident light coming through the relatively small aperture. We propose a low-light image processing framework that performs joint illumination adjustment, color enhancement, and denoising. Our framework does not need to recollect massive data when being adapted to another camera model.
  arXiv  Detail & Related papers  (2021-06-28T16:43:52Z)
• Unsupervised Low-light Image Enhancement with Decoupled Networks [103.74355338972123]
  We learn a two-stage GAN-based framework to enhance the real-world low-light images in a fully unsupervised fashion. Our proposed method outperforms the state-of-the-art unsupervised image enhancement methods in terms of both illumination enhancement and noise reduction.
  arXiv  Detail & Related papers  (2020-05-06T13:37:08Z)

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.