Results 1 to 10 of about 43,922 (147)
Underwater low-light enhancement network based on bright channel prior and attention mechanism. [PDF]
PLoS ONE, 2023 At present, there are some problems in underwater low light image, such as low contrast, blurred details, color distortion. In the process of low illumination image enhancement, there are often problems such as artifacts, loss of edge details and noise ...
Zhangjing Zheng, Xixia Huang, Le Wang
doaj +4 more sources
Haze Level Evaluation Using Dark and Bright Channel Prior Information
Atmosphere, 2022 Haze level evaluation is highly desired in outdoor scene monitoring applications. However, there are relatively few approaches available in this area. In this paper, a novel haze level evaluation strategy for real-world outdoor scenes is presented.
Ying Chu, Fan Chen, Hong Fu, Hengyong Yu
doaj +4 more sources
Nighttime low illumination image enhancement with single image using bright/dark channel prior [PDF]
EURASIP Journal on Image and Video Processing, 2018 Nighttime low illumination image enhancement is highly desired for outdoor computer vision applications. However, few works have been studied towards this goal. In addition, the low illumination enhancement problem becomes very challenging when the depth
Zhenghao Shi +4 more
doaj +5 more sources
Low-light image restoration using bright channel prior-based variational Retinex model [PDF]
EURASIP Journal on Image and Video Processing, 2017 This paper presents a low-light image restoration method based on the variational Retinex model using the bright channel prior (BCP) and total-variation minimization.
Seonhee Park +4 more
doaj +6 more sources
A low‐light image enhancement method based on bright channel prior and maximum colour channel [PDF]
IET Image Processing, 2021 Low‐light image enhancement algorithms have been introduced to improve the visual quality of low‐light images that may degrade the performance of many computer vision and multimedia systems designed for high‐quality images.
Ghada Sandoub +3 more
doaj +3 more sources
Single image dehazing based on bright channel prior model and saliency analysis strategy [PDF]
IET Image Processing, 2021 Haze is a common atmospheric phenomenon that causes poor visibility in outdoor images, which greatly limits image application in later stages. Therefore, haze removal has become the first and most indispensable step when dealing with degraded images.
Libao Zhang, Shan Wang, Xiaohan Wang
doaj +2 more sources
Single-Image Dehazing Based on Improved Bright Channel Prior and Dark Channel Prior
Electronics (Switzerland), 2023 Single-image dehazing plays a significant preprocessing role in machine vision tasks. As the dark-channel-prior method will fail in the sky region of the image, resulting in inaccurately estimated parameters, and given the failure of many methods to address a large band of haze, we propose a simple yet effective method for single-image dehazing based ...
Hao Zhou, Hailing Xiong
exaly +2 more sources
Dark and Bright Channel Prior Embedded Network for Dynamic Scene Deblurring [PDF]
IEEE Transactions on Image Processing, 2020 Recent years have witnessed the significant progress on convolutional neural networks (CNNs) in dynamic scene deblurring. While most of the CNN models are generally learned by the reconstruction loss defined on training data, incorporating suitable image priors as well as regularization terms into the network architecture could boost the deblurring ...
Wangmeng Zuo, Jianrui Cai
exaly +3 more sources
Bright Channel Prior Attention for Multispectral Pedestrian Detection
CoRR, 2023 Multispectral methods have gained considerable attention due to their promising performance across various fields. However, most existing methods cannot effectively utilize information from two modalities while optimizing time efficiency. These methods often prioritize accuracy or time efficiency, leaving room for improvement in their performance.
Chenhang Cui, Jinyu Xie, Yechenhao Yang
openaire +2 more sources
Region Adaptive Single Image Dehazing
Entropy, 2021 Image haze removal is essential in preprocessing for computer vision applications because outdoor images taken in adverse weather conditions such as fog or snow have poor visibility.
Changwon Kim
doaj +1 more source