Results 71 to 80 of about 22,622,900 (184)

Extreme Low-Light Image Enhancement for Surveillance Cameras Using Attention U-Net

Sensors, 2020
Low-light image enhancement is one of the most challenging tasks in computer vision, and it is actively researched and used to solve various problems. Most of the time, image processing achieves significant performance under normal lighting conditions ...
Sophy Ai, Jangwoo Kwon
doaj   +1 more source

Low-Light Image Segmentation on Edge Computing System

Sensors
Segmenting low-light images, such as images showing cracks on tunnel walls, is challenging due to limited visibility. Hence, we need to combine image brightness enhancement and a segmentation algorithm.
Sung-Chan Choi, Sung-Yeon Kim
doaj   +1 more source

Hierarchical flow learning for low-light image enhancement

Digital Communications and Networks
Low-light images often have defects such as low visibility, low contrast, high noise, and high color distortion compared with well-exposed images. If the low-light region of an image is enhanced directly, the noise will inevitably blur the whole image ...
Xinlin Yuan, Yong Wang, Yan Li, Hongbo Kang, Yu Chen, Boran Yang   +5 more
doaj   +1 more source

Interactive Evolutionary Algorithms for Image Enhancement and Creation [PDF]

, 2014
Image enhancement and creation, particularly for aesthetic purposes, are tasks for which the use of interactive evolutionary algorithms would seem to be well suited.
Mist, Joseph James
core  

Techniques in image restoration and enhancement [PDF]

, 1975
Includes bibliographical references.Image processing in its broad sense pervades many areas but it is convenient to group it into three main sections, viz: image coding, usually for image transmission over telecommunication links; pattern recognition for
Hepburn, John Stuart Andrew
core   +1 more source

Improving the perception of low-light enhanced images

Optics Express
Improving images captured under low-light conditions has become an important topic in computational color imaging, as it has a wide range of applications. Most current methods are either based on handcrafted features or on end-to-end training of deep neural networks that mostly focus on minimizing some distortion metric —such as PSNR or SSIM— on a set ...
Javier Vazquez-Corral, Graham D. Finlayson, Luis Herranz   +2 more
openaire   +4 more sources

Multi-branch low-light image iterative enhancement network

Scientific Reports
Images captured at night or under low-light conditions often suffer from insufficient brightness, low resolution, and detail loss. Although numerous deep learning-based methods have been proposed, most rely on direct mappings from low-illumination to ...
Yiwen Dou, Yiting Gao, Mei Gao, Senyan Zhao, Chenhao Zeng   +4 more
doaj   +1 more source

Unsupervised Low-light Image Enhancement Model with Adaptive Noise Suppression and Detail Preservation [PDF]

Jisuanji kexue
The visual quality of images taken under low-light environment is usually low,due to many factors such as low lightness and imaging noise.Current low-light image enhancement methods have a common limitation that they only focus on improving lightness ...
GAO Ren, HAO Shijie, GUO Yanrong
doaj   +1 more source

Progressive Two-Stage Network for Low-Light Image Enhancement

, 2021
At night, visual quality is reduced due to insufficient illumination so that it is difficult to conduct high-level visual tasks effectively. Existing image enhancement methods only focus on brightness improvement, however, improving image quality in low ...
Zhengxu Hua, Sirui Li, Yong Zhao, Yanpeng Sun, Zhanyou Chang   +4 more
core   +1 more source

SCAUNet: Step-Size-Consistent ADMM Unfolding Network for Low-Light Image Enhancement

Mathematics
Low-light image enhancement aims to restore visually pleasing normal-light images from degraded low-light observations. Most existing methods handle luminance variation from the enhancement perspective.
Xiaofang Li, Hongbiao Tian, Cui Fu
doaj   +1 more source