A review of advancements in low-light image enhancement using deep learning

• URL: http://arxiv.org/abs/2505.05759v2
• Date: Mon, 14 Jul 2025 02:31:12 GMT
• Title: A review of advancements in low-light image enhancement using deep learning
• Authors: Fangxue Liu, Lei Fan,
• Abstract summary: In low-light environments, the performance of computer vision algorithms adversely affects key vision tasks such as segmentation, detection, and classification.<n>With the rapid advancement of deep learning, its application to low-light image processing has attracted widespread attention.<n>This review provides detailed elaboration on how various recent approaches (from 2020) operate and their enhancement mechanisms.
• Score: 1.7930949972761197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In low-light environments, the performance of computer vision algorithms often deteriorates significantly, adversely affecting key vision tasks such as segmentation, detection, and classification. With the rapid advancement of deep learning, its application to low-light image processing has attracted widespread attention and seen significant progress in recent years. However, there remains a lack of comprehensive surveys that systematically examine how recent deep-learning-based low-light image enhancement methods function and evaluate their effectiveness in enhancing downstream vision tasks. To address this gap, this review provides detailed elaboration on how various recent approaches (from 2020) operate and their enhancement mechanisms, supplemented with clear illustrations. It also investigates the impact of different enhancement techniques on subsequent vision tasks, critically analyzing their strengths and limitations. Our review found that image enhancement improved the performance of downstream vision tasks to varying degrees. Although supervised methods often produced images with high perceptual quality, they typically produced modest improvements in vision tasks. In contrast, zero-shot learning, despite achieving lower scores in image quality metrics, showed consistently boosted performance across various vision tasks. These suggest a disconnect between image quality metrics and those evaluating vision task performance. Additionally, unsupervised domain adaptation techniques demonstrated significant gains in segmentation tasks, highlighting their potential in practical low-light scenarios where labelled data is scarce. Observed limitations of existing studies are analyzed, and directions for future research are proposed. This review serves as a useful reference for determining low-light image enhancement techniques and optimizing vision task performance in low-light conditions.

Related papers

• From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning [65.94580484237737]
  Low-light enhancement improves image quality for downstream tasks, but existing methods rely on physical or geometric priors.<n>We build a generalized bridge between low-light enhancement and low-light understanding, which we term Generalized Enhancement For Understanding (GEFU)<n>To address the diverse causes of low-light degradation, we leverage pretrained generative diffusion models to optimize images, achieving zero-shot generalization performance.
  arXiv  Detail & Related papers  (2025-07-11T07:51:26Z)
• A Comprehensive Survey on Image Signal Processing Approaches for Low-Illumination Image Enhancement [0.0]
  There is an increasing need for high-quality graphic information as people become more visually focused.<n> captured images frequently have poor visibility and a high amount of noise due to the limitations of image-capturing devices and lighting conditions.<n>Deep learning-based methods, however, have dominated recently made advances in this area.
  arXiv  Detail & Related papers  (2025-02-09T18:59:11Z)
• Leveraging Content and Context Cues for Low-Light Image Enhancement [25.97198463881292]
  Low-light conditions have an adverse impact on machine cognition, limiting the performance of computer vision systems in real life.<n>We propose to improve the existing zero-reference low-light enhancement by leveraging the CLIP model to capture image prior and for semantic guidance.<n>We experimentally show, that the proposed prior and semantic guidance help to improve the overall image contrast and hue, as well as improve background-foreground discrimination.
  arXiv  Detail & Related papers  (2024-12-10T17:32:09Z)
• HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning [62.264673293638175]
  Existing underwater image enhancement methods primarily focus on improving visual quality while overlooking practical implications.<n>We propose a invertible network for underwater perception enhancement, dubbed H, which enhances visual quality and demonstrates flexibility in handling other downstream tasks.
  arXiv  Detail & Related papers  (2024-11-27T12:37:03Z)
• Semi-LLIE: Semi-supervised Contrastive Learning with Mamba-based Low-light Image Enhancement [59.17372460692809]
  This work proposes a mean-teacher-based semi-supervised low-light enhancement (Semi-LLIE) framework that integrates the unpaired data into model training. We introduce a semantic-aware contrastive loss to faithfully transfer the illumination distribution, contributing to enhancing images with natural colors. We also propose novel perceptive loss based on the large-scale vision-language Recognize Anything Model (RAM) to help generate enhanced images with richer textual details.
  arXiv  Detail & Related papers  (2024-09-25T04:05:32Z)
• Low-Light Enhancement Effect on Classification and Detection: An Empirical Study [48.6762437869172]
  We evaluate the impact of Low-Light Image Enhancement (LLIE) methods on high-level vision tasks. Our findings suggest a disconnect between image enhancement for human visual perception and for machine analysis. This insight is crucial for the development of LLIE techniques that align with the needs of both human and machine vision.
  arXiv  Detail & Related papers  (2024-09-22T14:21:31Z)
• Unveiling Deep Shadows: A Survey and Benchmark on Image and Video Shadow Detection, Removal, and Generation in the Deep Learning Era [81.15890262168449]
  Shadows are created when light encounters obstacles, resulting in regions of reduced illumination.<n>This paper offers a benchmark on shadow detection, removal, and generation in both images and videos.<n>It focuses on the deep learning approaches of the past decade.
  arXiv  Detail & Related papers  (2024-09-03T17:59:05Z)
• Gap-closing Matters: Perceptual Quality Evaluation and Optimization of Low-Light Image Enhancement [55.8106019031768]
  There is a growing consensus in the research community that the optimization of low-light image enhancement approaches should be guided by the visual quality perceived by end users. We propose a gap-closing framework for assessing subjective and objective quality systematically. We validate the effectiveness of our proposed framework through both the accuracy of quality prediction and the perceptual quality of image enhancement.
  arXiv  Detail & Related papers  (2023-02-22T15:57:03Z)
• Self-Aligned Concave Curve: Illumination Enhancement for Unsupervised Adaptation [36.050270650417325]
  We propose a learnable illumination enhancement model for high-level vision. Inspired by real camera response functions, we assume that the illumination enhancement function should be a concave curve. Our model architecture and training designs mutually benefit each other, forming a powerful unsupervised normal-to-low light adaptation framework.
  arXiv  Detail & Related papers  (2022-10-07T19:32:55Z)
• The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement [17.874116769066095]
  There is an increasing consensus that the design and optimization of low light image enhancement methods need to be fully driven by perceptual quality.<n>We propose a loop enhancement framework that produces a clear picture of how the enhancement of low-light images could be optimized towards better visual quality.
  arXiv  Detail & Related papers  (2022-02-20T06:20:06Z)
• Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision [95.45256938467237]
  Images captured from low-light scenes often suffer from severe degradations. Deep learning methods have been proposed to enhance the visual quality of low-light images. It is still challenging to extend these enhancement techniques to handle other Low-Light Vision applications.
  arXiv  Detail & Related papers  (2021-12-09T06:08:31Z)
• Evaluating COPY-BLEND Augmentation for Low Level Vision Tasks [28.628939818365932]
  Copy-blend data augmentation technique copies patches from noisy images and blends onto a clean image to ensure that an underlying algorithm localizes and recovers affected regions resulting in increased perceptual quality of a recovered image. Report: improved performance, reduced requirement for training dataset, and early convergence across tasks such as low light image enhancement, image dehazing and image deblurring without any modification to baseline algorithm.
  arXiv  Detail & Related papers  (2021-03-10T06:17:52Z)
• Improving Aerial Instance Segmentation in the Dark with Self-Supervised Low Light Enhancement [6.500738558466833]
  Low light conditions in aerial images adversely affect the performance of vision based applications. We propose a new method that is capable of enhancing the low light image in a self-supervised fashion. We also propose the generation of a new low light aerial dataset using GANs.
  arXiv  Detail & Related papers  (2021-02-10T12:24:40Z)

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.