Anchor then Polish for Low-light Enhancement
- URL: http://arxiv.org/abs/2603.15472v1
- Date: Mon, 16 Mar 2026 16:06:30 GMT
- Title: Anchor then Polish for Low-light Enhancement
- Abstract summary: Low-light image enhancement is challenging due to entangled degradations.<n>This work proposes a novel anchor-then-polish (ATP) framework to fundamentally decouple global energy alignment from local detail refinement.<n>Our method achieves state-of-the-art performance, producing visually natural and quantitatively superior low-light enhancements.
- Score: 10.471765109354724
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Low-light image enhancement is challenging due to entangled degradations, mainly including poor illumination, color shifts, and texture interference. Existing methods often rely on complex architectures to address these issues jointly but may overfit simple physical constraints, leading to global distortions. This work proposes a novel anchor-then-polish (ATP) framework to fundamentally decouple global energy alignment from local detail refinement. First, macro anchoring is customized to (greatly) stabilize luminance distribution and correct color by learning a scene-adaptive projection matrix with merely 12 degrees of freedom, revealing that a simple linear operator can effectively align global energy. The macro anchoring then reduces the task to micro polishing, which further refines details in the wavelet domain and chrominance space under matrix guidance. A constrained luminance update strategy is designed to ensure global consistency while directing the network to concentrate on fine-grained polishing. Extensive experiments on multiple benchmarks show that our method achieves state-of-the-art performance, producing visually natural and quantitatively superior low-light enhancements.
Related papers
- Towards Controllable Low-Light Image Enhancement: A Continuous Multi-illumination Dataset and Efficient State Space Framework [2.977173671229414]
We introduce Controllable Low-light Enhancement (CLE), a holistic framework supported by Light100.<n>To resolve the conflict between luminance control and chromatic fidelity, a noise-decoupled supervision strategy is employed.<n>Our approach achieves competitive performance and robust controllability, providing a real-world multi-illumination alternative.
arXiv Detail & Related papers (2026-03-26T10:40:08Z) - 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) - Breaking Complexity Barriers: High-Resolution Image Restoration with Rank Enhanced Linear Attention [54.42902794496325]
Linear attention, a variant of softmax attention, demonstrates promise in global context modeling.<n>We propose Rank Enhanced Linear Attention (RELA), a simple yet effective method that enriches feature representations by integrating a lightweight depthwise convolution.<n>Building upon RELA, we propose an efficient and effective image restoration Transformer, named LAformer.
arXiv Detail & Related papers (2025-05-22T02:57:23Z) - Adaptive Low Light Enhancement via Joint Global-Local Illumination Adjustment [0.0]
We propose a novel brightness-adaptive enhancement framework to tackle the challenge of local exposure inconsistencies in low-light images.<n>Our method achieves superior quantitative and qualitative results compared to state-of-the-art algorithms.
arXiv Detail & Related papers (2025-04-01T03:46:28Z) - TSCnet: A Text-driven Semantic-level Controllable Framework for Customized Low-Light Image Enhancement [30.498816319802412]
We propose a new light enhancement task and a new framework that provides customized lighting control through prompt-driven, semantic-level, and quantitative brightness adjustments.<n> Experimental results on benchmark datasets demonstrate our framework's superior performance at increasing visibility, maintaining natural color balance, and amplifying fine details without creating artifacts.
arXiv Detail & Related papers (2025-03-11T08:30:50Z) - 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) - Fearless Luminance Adaptation: A Macro-Micro-Hierarchical Transformer
for Exposure Correction [65.5397271106534]
A single neural network is difficult to handle all exposure problems.
In particular, convolutions hinder the ability to restore faithful color or details on extremely over-/under- exposed regions.
We propose a Macro-Micro-Hierarchical transformer, which consists of a macro attention to capture long-range dependencies, a micro attention to extract local features, and a hierarchical structure for coarse-to-fine correction.
arXiv Detail & Related papers (2023-09-02T09:07:36Z) - Low-Light Image Enhancement with Illumination-Aware Gamma Correction and
Complete Image Modelling Network [69.96295927854042]
Low-light environments usually lead to less informative large-scale dark areas.
We propose to integrate the effectiveness of gamma correction with the strong modelling capacities of deep networks.
Because exponential operation introduces high computational complexity, we propose to use Taylor Series to approximate gamma correction.
arXiv Detail & Related papers (2023-08-16T08:46:51Z) - Cycle-Interactive Generative Adversarial Network for Robust Unsupervised
Low-Light Enhancement [109.335317310485]
Cycle-Interactive Generative Adversarial Network (CIGAN) is capable of not only better transferring illumination distributions between low/normal-light images but also manipulating detailed signals.
In particular, the proposed low-light guided transformation feed-forwards the features of low-light images from the generator of enhancement GAN into the generator of degradation GAN.
arXiv Detail & Related papers (2022-07-03T06:37:46Z)
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.