Evaluating Low-Light Image Enhancement Across Multiple Intensity Levels

• URL: http://arxiv.org/abs/2511.15496v1
• Date: Wed, 19 Nov 2025 14:52:51 GMT
• Title: Evaluating Low-Light Image Enhancement Across Multiple Intensity Levels
• Authors: Maria Pilligua, David Serrano-Lozano, Pai Peng, Ramon Baldrich, Michael S. Brown, Javier Vazquez-Corral,
• Abstract summary: We introduce the Multi-Illumination Low-Light dataset, containing images captured at diverse light intensities under controlled conditions.<n>We benchmark several state-of-the-art methods and reveal significant performance variations across intensity levels.<n>Our modifications achieve up to 10 dB PSNR improvement for DSLR and 2 dB for the smartphone on Full HD images.
• Score: 30.012366353273222
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Imaging in low-light environments is challenging due to reduced scene radiance, which leads to elevated sensor noise and reduced color saturation. Most learning-based low-light enhancement methods rely on paired training data captured under a single low-light condition and a well-lit reference. The lack of radiance diversity limits our understanding of how enhancement techniques perform across varying illumination intensities. We introduce the Multi-Illumination Low-Light (MILL) dataset, containing images captured at diverse light intensities under controlled conditions with fixed camera settings and precise illuminance measurements. MILL enables comprehensive evaluation of enhancement algorithms across variable lighting conditions. We benchmark several state-of-the-art methods and reveal significant performance variations across intensity levels. Leveraging the unique multi-illumination structure of our dataset, we propose improvements that enhance robustness across diverse illumination scenarios. Our modifications achieve up to 10 dB PSNR improvement for DSLR and 2 dB for the smartphone on Full HD images.

Related papers

• Unifying Color and Lightness Correction with View-Adaptive Curve Adjustment for Robust 3D Novel View Synthesis [73.27997579020233]
  We propose Luminance-GS++, a 3DGS-based framework for robust NVS under diverse illumination conditions.<n>Our method combines a globally view-adaptive lightness adjustment with a local pixel-wise residual refinement for precise color correction.
  arXiv  Detail & Related papers  (2026-02-20T16:20:50Z)
• Simultaneous Enhancement and Noise Suppression under Complex Illumination Conditions [18.76552485320789]
  We propose a novel framework for simultaneous enhancement and noise suppression under complex illumination conditions.<n>The proposed method is evaluated on real-world datasets obtained from practical applications.
  arXiv  Detail & Related papers  (2025-12-09T09:04:26Z)
• LightQANet: Quantized and Adaptive Feature Learning for Low-Light Image Enhancement [65.06462316546806]
  Low-light image enhancement aims to improve illumination while preserving high-quality color and texture.<n>Existing methods often fail to extract reliable feature representations due to severely degraded pixel-level information under low-light conditions.<n>We propose LightQANet, a novel framework that introduces quantized and adaptive feature learning for low-light enhancement.
  arXiv  Detail & Related papers  (2025-10-16T14:54:42Z)
• SAIGFormer: A Spatially-Adaptive Illumination-Guided Network for Low-Light Image Enhancement [58.79901582809091]
  Recent Transformer-based low-light enhancement methods have made promising progress in recovering global illumination.<n>Recent Transformer-based low-light enhancement methods have made promising progress in recovering global illumination.<n>We present a Spatially-Adaptive Illumination-Guided Transformer framework that enables accurate illumination restoration.
  arXiv  Detail & Related papers  (2025-07-21T11:38:56Z)
• SEE: See Everything Every Time - Adaptive Brightness Adjustment for Broad Light Range Images via Events [62.23917631871362]
  Event cameras, with a high dynamic range exceeding $120dB$, significantly outperform traditional embedded cameras.<n>We propose a novel research question: how to employ events to enhance and adaptively adjust the brightness of images captured under broad lighting conditions.<n>Our framework captures color through sensor patterns, uses cross-attention to model events as a brightness dictionary, and adjusts the image's dynamic range to form a broad light-range representation.
  arXiv  Detail & Related papers  (2025-02-28T14:55:37Z)
• LUMINA-Net: Low-light Upgrade through Multi-stage Illumination and Noise Adaptation Network for Image Enhancement [26.585985828583304]
  Low-light image enhancement (LLIE) is a crucial task in computer vision aimed at enhancing the visual fidelity of images captured under low-illumination conditions.<n>We propose LUMINA-Net, an unsupervised deep learning framework that learns adaptive priors from low-light image pairs by integrating multi-stage illumination and reflectance modules.
  arXiv  Detail & Related papers  (2025-02-21T03:37:58Z)
• A Non-Uniform Low-Light Image Enhancement Method with Multi-Scale Attention Transformer and Luminance Consistency Loss [11.585269110131659]
  Low-light image enhancement aims to improve the perception of images collected in dim environments. Existing methods cannot adaptively extract the differentiated luminance information, which will easily cause over-exposure and under-exposure. We propose a multi-scale attention Transformer named MSATr, which sufficiently extracts local and global features for light balance to improve the visual quality.
  arXiv  Detail & Related papers  (2023-12-27T10:07:11Z)
• Dimma: Semi-supervised Low Light Image Enhancement with Adaptive Dimming [0.728258471592763]
  Enhancing low-light images while maintaining natural colors is a challenging problem due to camera processing variations. We propose Dimma, a semi-supervised approach that aligns with any camera by utilizing a small set of image pairs. We achieve that by introducing a convolutional mixture density network that generates distorted colors of the scene based on the illumination differences.
  arXiv  Detail & Related papers  (2023-10-14T17:59:46Z)
• 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)
• 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.