Results 251 to 260 of about 850,785 (317)

SNR-Aware Low-light Image Enhancement

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
This paper presents a new solution for low-light image enhancement by collectively exploiting Signal-to-Noise-Ratio-aware transformers and convolutional models to dynamically enhance pixels with spatial-varying operations.
Xiaogang Xu, Ruixing Wang, Chiao Fu, Jiaya Jia   +3 more
semanticscholar   +2 more sources

Sparse Gradient Regularized Deep Retinex Network for Robust Low-Light Image Enhancement

IEEE Transactions on Image Processing, 2021
Due to the absence of a desirable objective for low-light image enhancement, previous data-driven methods may provide undesirable enhanced results including amplified noise, degraded contrast and biased colors.
Wenhan Yang, Wenjing Wang, Haofeng Huang, Shiqi Wang, Jiaying Liu   +4 more
semanticscholar   +3 more sources

Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model

IEEE Transactions on Image Processing, 2018
Low-light image enhancement methods based on classic Retinex model attempt to manipulate the estimated illumination and to project it back to the corresponding reflectance. However, the model does not consider the noise, which inevitably exists in images
Mading Li, Jiaying Liu, Wenhan Yang, Xiaoyan Sun, Zongming Guo   +4 more
semanticscholar   +3 more sources

RetinexDIP: A Unified Deep Framework for Low-Light Image Enhancement

IEEE Transactions on Circuits and Systems for Video Technology, 2022
Low-light images suffer from low contrast and unclear details, which not only reduces the available information for humans but limits the application of computer vision algorithms.
Zunjin Zhao, Bangshu Xiong, Lei Wang, Qiaofeng Ou, Lei Yu, Fa Kuang   +5 more
semanticscholar   +3 more sources

TBEFN: A Two-Branch Exposure-Fusion Network for Low-Light Image Enhancement

IEEE Transactions on Multimedia, 2021
Images obtained under low-light conditions are usually accompanied by varied and highly unpredictable degradation. The uncertainty of the imaging environment makes the enhancement even more challenging.
K. Lu, Lihong Zhang
semanticscholar   +3 more sources

Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement

IEEE International Conference on Computer Vision, 2023
When enhancing low-light images, many deep learning algorithms are based on the Retinex theory. However, the Retinex model does not consider the corruptions hidden in the dark or introduced by the light-up process.
Yuanhao Cai, Hao Bian, Jing Lin, Haoqian Wang, R. Timofte, Yulun Zhang   +5 more
semanticscholar   +1 more source

URetinex-Net: Retinex-based Deep Unfolding Network for Low-light Image Enhancement

Computer Vision and Pattern Recognition, 2022
Retinex model-based methods have shown to be effective in layer-wise manipulation with well-designed priors for low-light image enhancement. However, the commonly used handcrafted priors and optimization-driven solutions lead to the absence of adaptivity
Wen-Bin Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, Jianmin Jiang   +5 more
semanticscholar   +1 more source

Polarization-Aware Low-Light Image Enhancement

Proceedings of the AAAI Conference on Artificial Intelligence, 2023
Polarization-based vision algorithms have found uses in various applications since polarization provides additional physical constraints. However, in low-light conditions, their performance would be severely degenerated since the captured polarized images could be noisy, leading to noticeable degradation in the degree of polarization (DoP) and the ...
Chu Zhou, Minggui Teng, Youwei Lyu, Si Li, Chao Xu, Boxin Shi   +5 more
openaire   +1 more source

Edge Guided Low-Light Image Enhancement

2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), 2021
Images captured under poorly lit environments often suffer from poor brightness, poor contrast, unwanted noise and color distortion. To enhance these images, there is a need to provide additional information while also reducing the burden of generalization on a single network.
Deepanshu Rana, Kanishk Jayant Lal, Anil Singh Parihar   +2 more
openaire   +1 more source

From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement

Computer Vision and Pattern Recognition, 2020
Under-exposure introduces a series of visual degradation, i.e. decreased visibility, intensive noise, and biased color, etc. To address these problems, we propose a novel semi-supervised learning approach for low-light image enhancement. A deep recursive
Wenhan Yang, Shiqi Wang, Yuming Fang, Yue Wang, Jiaying Liu   +4 more
semanticscholar   +1 more source