VCR: Variance-Driven Channel Recalibration for Robust Low-Light Enhancement

• URL: http://arxiv.org/abs/2603.10975v1
• Date: Tue, 10 Mar 2026 15:10:44 GMT
• Title: VCR: Variance-Driven Channel Recalibration for Robust Low-Light Enhancement
• Abstract summary: The Variance-Driven Channel Recalibration for Robust Low-Light Enhancement (VCR) is a novel framework for low-light image enhancement.<n>The VCR consists of two main components, including the Channel Adaptive Adjustment (CAA) module.<n>It employs variance-guided feature filtering to enhance the model's focus on regions with high intensity and color distribution.<n>It achieves state-of-the-art performance compared with existing methods.
• Score: 6.074987745318463
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most sRGB-based LLIE methods suffer from entangled luminance and color, while the HSV color space offers insufficient decoupling at the cost of introducing significant red and black noise artifacts. Recently, the HVI color space has been proposed to address these limitations by enhancing color fidelity through chrominance polarization and intensity compression. However, existing methods could suffer from channel-level inconsistency between luminance and chrominance, and misaligned color distribution may lead to unnatural enhancement results. To address these challenges, we propose the Variance-Driven Channel Recalibration for Robust Low-Light Enhancement (VCR), a novel framework for low-light image enhancement. VCR consists of two main components, including the Channel Adaptive Adjustment (CAA) module, which employs variance-guided feature filtering to enhance the model's focus on regions with high intensity and color distribution. And the Color Distribution Alignment (CDA) module, which enforces distribution alignment in the color feature space. These designs enhance perceptual quality under low-light conditions. Experimental results on several benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance compared with existing methods.

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)
• Towards Color-Faithful Low-Light Image Enhancement via Adaptive Color Debiasing and Saturation Rectification [17.063732568364927]
  CAGE is a cylindrical color correction framework with adaptive color debiasing and gamut-harmonized saturation rectification.<n>CAGE achieves more faithful color restoration, specifically reduces color bias and saturation abnormality, and delivers better overall visual quality.
  arXiv  Detail & Related papers  (2026-08-11T05:37:54Z)
• RHVI-FDD: A Hierarchical Decoupling Framework for Low-Light Image Enhancement [11.50861592672004]
  Low-light images often suffer from severe noise, detail loss, and color distortion.<n>We propose a novel hierarchical decoupling framework (RHVI-FDD) to solve this problem.<n>Our method consistently outperforms existing state-of-the-art approaches in both objective metrics and subjective visual quality.
  arXiv  Detail & Related papers  (2026-04-07T12:18:57Z)
• Revisiting Lightweight Low-Light Image Enhancement: From a YUV Color Space Perspective [17.507319835166406]
  We propose a novel YUV-based paradigm that strategically restores channels using a Dual-Stream Global-Local Attention module for the Y channel, a Y-guided Local-Aware Frequency Attention module for the UV channels, and a Guided Interaction module for final feature fusion.<n>Our model establishes a new state-of-the-art on multiple benchmarks, delivering superior visual quality with a significantly lower parameter count.
  arXiv  Detail & Related papers  (2026-01-24T07:27:54Z)
• Perception-Inspired Color Space Design for Photo White Balance Editing [20.114221919949603]
  White balance (WB) is a key step in the image signal processor (ISP) pipeline.<n>We propose a novel framework for WB correction that leverages a perception-inspired Learnable HSI (LHSI) color space.
  arXiv  Detail & Related papers  (2025-12-10T07:27:16Z)
• HVI-CIDNet+: Beyond Extreme Darkness for Low-Light Image Enhancement [56.02740727422916]
  Low-Light Image Enhancement (LLIE) aims to restore vivid content and details from corrupted low-light images.<n>Existing standard RGB (sRGB) color space-based LLIE methods often produce color bias and brightness artifacts.<n>We propose a new color space for LLIE, defined by the HV color map and learnable intensity.<n>HVI-CIDNet+ is built upon the HVI color space to restore damaged content and mitigate color distortion in extremely dark regions.
  arXiv  Detail & Related papers  (2025-07-09T13:03:34Z)
• Contourlet Refinement Gate Framework for Thermal Spectrum Distribution Regularized Infrared Image Super-Resolution [54.293362972473595]
  Image super-resolution (SR) aims to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts. Current approaches to address SR tasks are either dedicated to extracting RGB image features or assuming similar degradation patterns. We propose a Contourlet refinement gate framework to restore infrared modal-specific features while preserving spectral distribution fidelity.
  arXiv  Detail & Related papers  (2024-11-19T14:24:03Z)
• Sagiri: Low Dynamic Range Image Enhancement with Generative Diffusion Prior [26.93312785388343]
  High Dynamic Range scenery using 8-bit cameras often suffers from over-/underexposure, loss of fine details due to low bit-depth compression, skewed color distributions, and strong noise in dark areas. Traditional LDR image enhancement methods primarily focus on color mapping, which enhances the visual representation by expanding the image's color range and adjusting the brightness. We propose a novel two-stage approach to address the full scope of challenges in HDR imaging and surpass the limitations of current models.
  arXiv  Detail & Related papers  (2024-06-13T17:58:40Z)
• You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement [50.37253008333166]
  Low-Light Image Enhancement (LLIE) task tends to restore the details and visual information from corrupted low-light images. We propose a novel trainable color space, named Horizontal/Vertical-Intensity (HVI) It not only decouples brightness and color from RGB channels to mitigate the instability during enhancement but also adapts to low-light images in different illumination ranges due to the trainable parameters.
  arXiv  Detail & Related papers  (2024-02-08T16:47:43Z)
• 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)
• Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding [88.46682991985907]
  We present an underwater image enhancement network via medium transmission-guided multi-color space embedding, called Ucolor. Our network can effectively improve the visual quality of underwater images by exploiting multiple color spaces embedding.
  arXiv  Detail & Related papers  (2021-04-27T07:35:30Z)

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.