scholarly journals Low Light Image Enhancement using Convolutional Neural Network

2021 ◽  
Vol 9 (VI) ◽  
pp. 3463-3472
Author(s):  
Choundur Vishnu
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Convolutional Neural Network ◽  
Image Enhancement ◽  
Scene Understanding ◽  
Object Location ◽  
Low Light ◽  
Light Image ◽  
Image Improvement

Great quality images and pictures are remarkable for some perceptions. Nonetheless, not each and every images are in acceptable features and quality as they are capture in non-identical light atmosphere. At the point when an image is capture in a low light state the pixel esteems are in a low-esteem range, which will cause image quality to decrease evidently. Since the entire image shows up dull, it's difficult to recognize items or surfaces clearly. Thus, it is vital to improve the nature of low-light images. Low light image enhancement is required in numerous PC vision undertakings for object location and scene understanding. In some cases there is a condition when image caught in low light consistently experience the ill effects of low difference and splendor which builds the trouble of resulting undeniable level undertaking in incredible degree. Low light image improvement utilizing convolutional neural network framework accepts dull or dark images as information and creates brilliant images as a yield without upsetting the substance of the image. So understanding the scene caught through image becomes simpler task.

Low-light image enhancement of high-speed endoscopic videos using a convolutional neural network

2019 ◽  
Vol 57 (7) ◽  
pp. 1451-1463 ◽  
Author(s):  
Pablo Gómez ◽  
Marion Semmler ◽  
Anne Schützenberger ◽  
Christopher Bohr ◽  
Michael Döllinger
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Enhancement ◽  
High Speed ◽  
Low Light ◽  
Light Image ◽  
Endoscopic Videos

Low-Light Image Enhancement Based on Deep Convolutional Neural Network

2019 ◽  
Vol 39 (2) ◽  
pp. 0210004 ◽  
Author(s):  
马红强 Ma Hongqiang ◽  
马时平 Ma Shiping ◽  
许悦雷 Xu Yuelei ◽  
朱明明 Zhu Mingming
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Enhancement ◽  
Deep Convolutional Neural Network ◽  
Low Light ◽  
Light Image

Low-Light Image Enhancement Based on Multi-Branch All Convolutional Neural Network

2020 ◽  
Vol 57 (14) ◽  
pp. 141021
Author(s):  
吴若有 Wu Ruoyou ◽  
王德兴 Wang Dexing ◽  
袁红春 Yuan Hongchun ◽  
宫鹏 Gong Peng ◽  
陈冠奇 Chen Guanqi ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Enhancement ◽  
Low Light ◽  
Light Image

Perceptual Quality Assessment of Low-light Image Enhancement

2021 ◽  
Vol 17 (4) ◽  
pp. 1-24
Author(s):  
Guangtao Zhai ◽  
Wei Sun ◽  
Xiongkuo Min ◽  
Jiantao Zhou
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Enhancement ◽  
Dynamic Range ◽  
State Of The Art ◽  
Perceptual Quality ◽  
Low Light ◽  
Light Image ◽  
Light Enhancement

Low-light image enhancement algorithms (LIEA) can light up images captured in dark or back-lighting conditions. However, LIEA may introduce various distortions such as structure damage, color shift, and noise into the enhanced images. Despite various LIEAs proposed in the literature, few efforts have been made to study the quality evaluation of low-light enhancement. In this article, we make one of the first attempts to investigate the quality assessment problem of low-light image enhancement. To facilitate the study of objective image quality assessment (IQA), we first build a large-scale low-light image enhancement quality (LIEQ) database. The LIEQ database includes 1,000 light-enhanced images, which are generated from 100 low-light images using 10 LIEAs. Rather than evaluating the quality of light-enhanced images directly, which is more difficult, we propose to use the multi-exposure fused (MEF) image and stack-based high dynamic range (HDR) image as a reference and evaluate the quality of low-light enhancement following a full-reference (FR) quality assessment routine. We observe that distortions introduced in low-light enhancement are significantly different from distortions considered in traditional image IQA databases that are well-studied, and the current state-of-the-art FR IQA models are also not suitable for evaluating their quality. Therefore, we propose a new FR low-light image enhancement quality assessment (LIEQA) index by evaluating the image quality from four aspects: luminance enhancement, color rendition, noise evaluation, and structure preserving, which have captured the most key aspects of low-light enhancement. Experimental results on the LIEQ database show that the proposed LIEQA index outperforms the state-of-the-art FR IQA models. LIEQA can act as an evaluator for various low-light enhancement algorithms and systems. To the best of our knowledge, this article is the first of its kind comprehensive low-light image enhancement quality assessment study.

scholarly journals A Pipeline Neural Network for Low-Light Image Enhancement

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 13737-13744 ◽  
Author(s):  
Yanhui Guo ◽  
Xue Ke ◽  
Jie Ma ◽  
Jun Zhang
Keyword(s):  
Neural Network ◽  
Image Enhancement ◽  
Low Light ◽  
Light Image

scholarly journals Variance Reduction in Low Light Image Enhancement Model

2020 ◽  
Vol 9 (4) ◽  
pp. 139-142
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Image Enhancement ◽  
Variance Reduction ◽  
Low Light ◽  
Light Image ◽  
Hidden Layer

In image processing, enhancement of images taken in low light is considered to be a tricky and intricate process, especially for the images captured at nighttime. It is because various factors of the image such as contrast, sharpness and color coordination should be handled simultaneously and effectively. To reduce the blurs or noises on the low-light images, many papers have contributed by proposing different techniques. One such technique addresses this problem using a pipeline neural network. Due to some irregularity in the working of the pipeline neural networks model [1], a hidden layer is added to the model which results in a decrease in irregularity.

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