Results 251 to 260 of about 842,500 (318)

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

Semantically-guided low-light image enhancement

Pattern Recognition Letters, 2020
Abstract Recently, extensive research efforts have been made on low-light image enhancement. Many novel models have been proposed, such as the ones based on the Retinex theory, multiple exposure fusion, and deep neural networks. However, current models do not directly consider the semantic information in the modeling process.
Junyi Xie, Hao Bian, Yuanhang Wu, Yu Zhao, Linmin Shan, Shijie Hao   +5 more
openaire   +1 more source

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   +1 more source

Implicit Neural Representation for Cooperative Low-light Image Enhancement

IEEE International Conference on Computer Vision, 2023
The following three factors restrict the application of existing low-light image enhancement methods: unpredictable brightness degradation and noise, inherent gap between metric-favorable and visual-friendly versions, and the limited paired training data.
Shuzhou Yang, M. Ding, Yanmin Wu, Zihan Li, Jian Zhang   +4 more
semanticscholar   +1 more source

DSLR: Deep Stacked Laplacian Restorer for Low-Light Image Enhancement

IEEE transactions on multimedia, 2021
Various images captured in complicated lighting conditions often suffer from deterioration of the image quality. Such poor quality not only dissatisfies the user expectation but also may lead to a significant performance drop in many applications.
Seokjae Lim, Wonjun Kim
semanticscholar   +1 more source

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

IEEE transactions on circuits and systems for video technology (Print), 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   +1 more source

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   +1 more source

HVI: A New Color Space for Low-light Image Enhancement

Computer Vision and Pattern Recognition
Low-Light Image Enhancement (LLIE) is a crucial computer vision task that aims to restore detailed visual information from corrupted low-light images. Many existing LLIE methods are based on standard RGB (sRGB) space, which often produce color bias and ...
Qingsen Yan, Yixu Feng, Cheng Zhang, Guansong Pang, Kangbiao Shi, Peng Wu, Wei Dong, Jinqiu Sun, Yanning Zhang   +8 more
semanticscholar   +1 more source

Extracting Noise and Darkness: Low-Light Image Enhancement via Dual Prior Guidance

IEEE transactions on circuits and systems for video technology (Print)
The complex entanglement between darkness and noise hinders the advance of low-light image enhancement. Most existing methods adopted lightening-then-denoising or embedded a special denoising module into enhancement network without specific noise ...
Huake Wang, Xiaoyang Yan, Xingsong Hou, Kaibing Zhang, Yujie Dun   +4 more
semanticscholar   +1 more source