Toward Fast, Flexible, and Robust Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2204.10137v1
• Date: Thu, 21 Apr 2022 14:40:32 GMT
• Title: Toward Fast, Flexible, and Robust Low-Light Image Enhancement
• Authors: Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, Zhongxuan Luo
• Abstract summary: We develop a new Self-Calibrated Illumination (SCI) learning framework for fast, flexible, and robust brightening images in real-world low-light scenarios. Considering the computational burden of the cascaded pattern, we construct the self-calibrated module which realizes the convergence between results of each stage. We make comprehensive explorations to SCI's inherent properties including operation-insensitive adaptability and model-irrelevant generality.
• Score: 87.27326390675155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing low-light image enhancement techniques are mostly not only difficult to deal with both visual quality and computational efficiency but also commonly invalid in unknown complex scenarios. In this paper, we develop a new Self-Calibrated Illumination (SCI) learning framework for fast, flexible, and robust brightening images in real-world low-light scenarios. To be specific, we establish a cascaded illumination learning process with weight sharing to handle this task. Considering the computational burden of the cascaded pattern, we construct the self-calibrated module which realizes the convergence between results of each stage, producing the gains that only use the single basic block for inference (yet has not been exploited in previous works), which drastically diminishes computation cost. We then define the unsupervised training loss to elevate the model capability that can adapt to general scenes. Further, we make comprehensive explorations to excavate SCI's inherent properties (lacking in existing works) including operation-insensitive adaptability (acquiring stable performance under the settings of different simple operations) and model-irrelevant generality (can be applied to illumination-based existing works to improve performance). Finally, plenty of experiments and ablation studies fully indicate our superiority in both quality and efficiency. Applications on low-light face detection and nighttime semantic segmentation fully reveal the latent practical values for SCI. The source code is available at https://github.com/vis-opt-group/SCI.

Related papers

• Fast Context-Based Low-Light Image Enhancement via Neural Implicit Representations [6.113035634680655]
  Current deep learning-based low-light image enhancement methods often struggle with high-resolution images. We introduce a novel approach termed CoLIE, which redefines the enhancement process through mapping the 2D coordinates of an underexposed image to its illumination component.
  arXiv  Detail & Related papers  (2024-07-17T11:51:52Z)
• GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrieval [80.96706764868898]
  We present a new Low-light Image Enhancement (LLIE) network via Generative LAtent feature based codebook REtrieval (GLARE) We develop a generative Invertible Latent Normalizing Flow (I-LNF) module to align the LL feature distribution to NL latent representations, guaranteeing the correct code retrieval in the codebook. Experiments confirm the superior performance of GLARE on various benchmark datasets and real-world data.
  arXiv  Detail & Related papers  (2024-07-17T09:40:15Z)
• Unsupervised Image Prior via Prompt Learning and CLIP Semantic Guidance for Low-Light Image Enhancement [25.97198463881292]
  We propose to improve the zero-reference low-light enhancement method by leveraging the rich visual-linguistic CLIP prior. We show that the proposed method leads to consistent improvements across various datasets regarding task-based performance.
  arXiv  Detail & Related papers  (2024-05-19T08:06:14Z)
• Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method [51.30748775681917]
  We consider the task of low-light image enhancement (LLIE) and introduce a large-scale database consisting of images at 4K and 8K resolution. We conduct systematic benchmarking studies and provide a comparison of current LLIE algorithms. As a second contribution, we introduce LLFormer, a transformer-based low-light enhancement method.
  arXiv  Detail & Related papers  (2022-12-22T09:05:07Z)
• 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)
• Low-light Image Enhancement by Retinex Based Algorithm Unrolling and Adjustment [50.13230641857892]
  We propose a new deep learning framework for the low-light image enhancement (LIE) problem. The proposed framework contains a decomposition network inspired by algorithm unrolling, and adjustment networks considering both global brightness and local brightness sensitivity. Experiments on a series of typical LIE datasets demonstrated the effectiveness of the proposed method, both quantitatively and visually, as compared with existing methods.
  arXiv  Detail & Related papers  (2022-02-12T03:59:38Z)
• Semantically Contrastive Learning for Low-light Image Enhancement [48.71522073014808]
  Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images. We propose an effective semantically contrastive learning paradigm for LLE (namely SCL-LLE) Our method surpasses the state-of-the-arts LLE models over six independent cross-scenes datasets.
  arXiv  Detail & Related papers  (2021-12-13T07:08:33Z)
• 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)

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.