Beyond Illumination: A Conditional Mutual Information-Guided Network for Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2608.01886v1
• Date: Mon, 03 Aug 2026 08:27:38 GMT
• Title: Beyond Illumination: A Conditional Mutual Information-Guided Network for Low-Light Image Enhancement
• Abstract summary: Low-light image enhancement (LLIE) seeks to restore structural fidelity, natural color rendition, and proper exposure from images captured under inadequate lighting conditions.<n>Recent state-of-the-art approaches, such as CIDNet, adopt a dual-branch architecture comprising a chrominance (HV) branch and an intensity (I) branch to separately model decoupled and luminance information within the HVI color space.<n>These methods overlook the mutual interaction between intensity and chrominance components, which inherently limits their representational capacity and leads to suboptimal enhancement performance.
• Score: 48.364582443938296
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-light image enhancement (LLIE) seeks to restore structural fidelity, natural color rendition, and proper exposure from images captured under inadequate lighting conditions. Recent state-of-the-art approaches, such as CIDNet, adopt a dual-branch architecture comprising a chrominance (HV) branch and an intensity (I) branch to separately model decoupled chromatic and luminance information within the HVI color space. However, these methods overlook the mutual interaction between intensity and chrominance components, which inherently limits their representational capacity and leads to suboptimal enhancement performance. To address this limitation, we propose the Conditional Mutual Information-Guided Network (CMIG-Net), which leverages conditional mutual information as a principled metric to quantitatively assess the contribution of chrominance features conditioned on the available intensity information. In particular, we design a Conditional Mutual Information Calibration (CMIC) module that generates a conditional information map, enabling region-adaptive recalibration of chrominance representations according to local illumination statistics. Furthermore, we introduce a Dynamic Dual-branch Information Restoration (D2IR) module, which adaptively governs bidirectional information flow between the intensity and chrominance branches, guided by both the conditional prior and the instantaneous restoration state. Extensive experiments on paired LLIE benchmarks demonstrate that CMIG-Net consistently outperforms CIDNet, achieving up to a 0.619 dB gain in PSNR, with a 0.382 dB improvement specifically on the challenging Sony-Total-Dark dataset.

Related papers

• BC-IHV: Conditioning the Color Space for Stable Rectified-Flow Low-Light Enhancement [51.79298431282401]
  Low-light image enhancement must correct ambiguous exposure without overwriting structure already supported by the input.<n>We propose Structure-Anchored Rectified Flow (SA-RF), which maintains correspondence through separate chromaticity/intensity stems.<n>We also introduce BC-IHV, a learnable Box--Cox polar color space whose analytically invertible intensity mapping controls the inverse-gradient dynamic range through a single exponent.
  arXiv  Detail & Related papers  (2026-08-22T08:43:00Z)
• UBLLIE: Unified Backlight and Low-Light Image Enhancement [15.386282490743838]
  Backlit and low-light images often suffer from severe exposure imbalance or global underexposure.<n>We propose a unified, unsupervised enhancement framework that addresses both types of degradation.<n>Our framework consistently outperforms state-of-the-art supervised and unsupervised methods in terms of fidelity, perceptual quality, and generalization.
  arXiv  Detail & Related papers  (2026-08-05T04:17:05Z)
• DRPFNet: Dual-domain Residual Progressive Fusion Network for RGB-Thermal Object Detection [13.47934602159213]
  RGB-thermal (RGB-T) object detection aims to fuse complementary information from visible and thermal modalities to achieve robust detection under varying illumination and weather conditions.<n>Current methods typically employ attention mechanisms or transformers to perform cross-modal fusion independently at each feature scale.<n>We propose DRPFNet, a Dual-domain Residual Progressive Fusion Network that constructs a unified information flow optimization system from three synergistic levels:structure, feature, and enhancement.
  arXiv  Detail & Related papers  (2026-08-04T09:20:31Z)
• OmniLight: One Model to Rule All Lighting Conditions [20.84793675424915]
  Adverse lighting conditions pose significant challenges to computer vision systems by degrading visibility and color fidelity.<n>We present a comprehensive study on lighting-related image restoration by exploring two contrasting strategies.<n>We discuss the impact of data distribution on the performance of specialized and unified architectures in lighting-related image restoration.
  arXiv  Detail & Related papers  (2026-04-16T15:47:49Z)
• ICLR: Inter-Chrominance and Luminance Interaction for Natural Color Restoration in Low-Light Image Enhancement [43.13923532996694]
  Low-Light Image Enhancement (LLIE) task aims at improving contrast while restoring details and textures for images captured in low-light conditions.<n>HVI color space has made significant progress in this task by enabling precise decoupling of chrominance and luminance.<n>We propose an Inter-Chrominance and Luminance Interaction (ICLR) framework including a Dual-stream Interaction Enhancement Module (DIEM) and a Covariance Correction Loss (CCL)
  arXiv  Detail & Related papers  (2025-11-17T17:12:46Z)
• Towards Generalized Range-View LiDAR Segmentation in Adverse Weather [65.22588361803942]
  We identify and analyze the unique challenges that affect the generalization of range-view LiDAR segmentation in severe weather.<n>We propose a modular and lightweight framework that enhances robustness without altering the core architecture of existing models.<n>Our approach significantly improves generalization to adverse weather with minimal inference overhead.
  arXiv  Detail & Related papers  (2025-06-10T16:48:27Z)
• 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)
• 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)
• CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement [97.95330185793358]
  Low-light image enhancement (LLIE) aims to improve low-illumination images.<n>Existing methods face two challenges: uncertainty in restoration from diverse brightness degradations and loss of texture and color information.<n>We propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement.
  arXiv  Detail & Related papers  (2024-04-08T07:34:39Z)
• VQCNIR: Clearer Night Image Restoration with Vector-Quantized Codebook [16.20461368096512]
  Night photography often struggles with challenges like low light and blurring, stemming from dark environments and prolonged exposures. We believe in the strength of data-driven high-quality priors and strive to offer a reliable and consistent prior, circumventing the restrictions of manual priors. We propose Clearer Night Image Restoration with Vector-Quantized Codebook (VQCNIR) to achieve remarkable and consistent restoration outcomes on real-world and synthetic benchmarks.
  arXiv  Detail & Related papers  (2023-12-14T02:16:27Z)
• Diving into Darkness: A Dual-Modulated Framework for High-Fidelity Super-Resolution in Ultra-Dark Environments [51.58771256128329]
  This paper proposes a specialized dual-modulated learning framework that attempts to deeply dissect the nature of the low-light super-resolution task. We develop Illuminance-Semantic Dual Modulation (ISDM) components to enhance feature-level preservation of illumination and color details. Comprehensive experiments showcases the applicability and generalizability of our approach to diverse and challenging ultra-low-light conditions.
  arXiv  Detail & Related papers  (2023-09-11T06:55:32Z)
• Enhancing Low-light Light Field Images with A Deep Compensation Unfolding Network [52.77569396659629]
  This paper presents the deep compensation network unfolding (DCUNet) for restoring light field (LF) images captured under low-light conditions. The framework uses the intermediate enhanced result to estimate the illumination map, which is then employed in the unfolding process to produce a new enhanced result. To properly leverage the unique characteristics of LF images, this paper proposes a pseudo-explicit feature interaction module.
  arXiv  Detail & Related papers  (2023-08-10T07:53:06Z)

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.