Zobrazeno 1 - 10
of 1 171
pro vyhledávání: '"Low-light Image Enhancement"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract Low-light image enhancement aims to enhance the visibility and contrast of low-light images while eliminating complex degradation issues such as noise, artifacts, and color distortions. Most existing low-light image enhancement methods eithe
Externí odkaz:
https://doaj.org/article/403ed3a3af2d4fc98b1b99273cdde8af
Publikováno v:
Gong-kuang zidonghua, Vol 50, Iss 10, Pp 29-37 (2024)
The working environment of mining unmanned vehicles features complex lighting conditions, leading to frequent occurrences of missed detections in pedestrian detection, which undermines the reliability and safety of these vehicles. To address the chal
Externí odkaz:
https://doaj.org/article/42438d3aeca64c73abb2b9aceaa81ac3
Publikováno v:
IET Image Processing, Vol 18, Iss 11, Pp 3028-3041 (2024)
Abstract Low‐light images are captured in environments with minimal lighting, such as nighttime or underwater conditions. These images often suffer from issues like low brightness, poor contrast, lack of detail, and overall darkness, significantly
Externí odkaz:
https://doaj.org/article/a61d2fe688554a4b9494bf0cefe8788d
Autor:
Wong, Johnny Kwok Wai, Maghrebi, Mojtaba, Ahmadian Fard Fini, Alireza, Alizadeh Golestani, Mohammad Amin, Ahmadnia, Mahdi, Er, Michael
Publikováno v:
Construction Innovation, 2022, Vol. 24, Issue 2, pp. 470-491.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/CI-02-2022-0044
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 3, Pp 4019-4032 (2024)
Abstract Intelligent monitoring technology plays an important role in promoting the development of coal mine safety management. Low illumination in the coal mine underground leads to difficult recognition of monitoring images and poor personnel detec
Externí odkaz:
https://doaj.org/article/fbadf828f8044ece8bd1964ed170477a
Publikováno v:
IEEE Access, Vol 12, Pp 179252-179264 (2024)
Unsupervised methods are gradually becoming a research hotspot in low-light image enhancement due to their lack of paired training data and higher quality visual enhancement effects, but existing unsupervised low-light image enhancement methods gener
Externí odkaz:
https://doaj.org/article/8ead61e711464d8c8757b2223ebbaa6c
Autor:
Mohan Yin, Jianbai Yang
Publikováno v:
IEEE Access, Vol 12, Pp 140185-140210 (2024)
Images captured under the influence of external factors (such as low light, nighttime, complex weather conditions, etc.) often exhibit unpleasant visual effects. Previous image enhancement methods have overly focused on improving brightness, neglecti
Externí odkaz:
https://doaj.org/article/47e2433affb64d6aa4378a863361c67d
Publikováno v:
IEEE Access, Vol 12, Pp 132891-132903 (2024)
Low-light images are susceptible to poor visibility, low contrast, and additive noise. Although various techniques can enhance brightness to a reasonable extent, they inevitably amplify noise and cause color distortion, halo artifacts, and other form
Externí odkaz:
https://doaj.org/article/2b8dddf0fcc64cc5847d4c0d6a499d1c
Autor:
Jun Zhu, Fenglian Liu, Zhihang Xue, Wenwei Luo, Haoran Peng, Jun He, Zhengzheng Fu, Donghui Luo
Publikováno v:
IEEE Access, Vol 12, Pp 106013-106024 (2024)
Ensuring the stability of cable tunnels is crucial for power safety and urban reliability in light of the increasing demand for urban electricity. However, frequent waterlogging incidents within tunnels pose a significant threat to city safety by dis
Externí odkaz:
https://doaj.org/article/8e48402b7b614a89aba22a4d5cfd209d
Publikováno v:
IEEE Access, Vol 12, Pp 105674-105685 (2024)
Low-light images captured at night often suffer from improper exposure, color distortion, and noise, which degrades the image quality and have a negative influence on subsequent applications. Many existing deep learning-based methods enhance low-ligh
Externí odkaz:
https://doaj.org/article/585892bed93945768dad805a1c3e5a2c