Results 31 to 40 of about 691 (187)

Semi‐supervised learning dehazing algorithm based on the OSV model

IET Image Processing, 2023
Despite the great progress that has been made in the task of single image dehazing, the results of the existing models in restoring image edge and texture information are still challenging.
Lijun Zhu, Weibo Wei, Zhenkuan Pan, Lianshun Ji, Jintao Song, Jinhan Li   +5 more
doaj   +1 more source

Robust Dehaze Algorithm for Degraded Image of CMOS Image Sensors

Sensors, 2017
The CMOS (Complementary Metal-Oxide-Semiconductor) is a new type of solid image sensor device widely used in object tracking, object recognition, intelligent navigation fields, and so on.
Chen Qu, Du-Yan Bi, Ping Sui, Ai-Nong Chao, Yun-Fei Wang   +4 more
doaj   +1 more source

Progressive Back-Traced Dehazing Network Based on Multi-Resolution Recurrent Reconstruction

IEEE Access, 2020
In order to alleviate adverse impacts of haze on high-level vision tasks, image dehazing attracts great attention from computer vision research field in recent years.
Qiaosi Yi, Aiwen Jiang, Juncheng Li, Jianyi Wan, Mingwen Wang   +4 more
doaj   +1 more source

A Ship Tracking and Speed Extraction Framework in Hazy Weather Based on Deep Learning

Journal of Marine Science and Engineering, 2023
Obtaining ship navigation information from maritime videos can significantly improve maritime supervision efficiency and enable timely safety warnings. Ship detection and tracking are essential technologies for mining video information.
Zhenzhen Zhou, Jiansen Zhao, Xinqiang Chen, Yanjun Chen   +3 more
doaj   +1 more source

NTIRE 2020 Challenge on NonHomogeneous Dehazing

, 2020
This paper reviews the NTIRE 2020 Challenge on NonHomogeneous Dehazing of images (restoration of rich details in hazy image). We focus on the proposed solutions and their results evaluated on NH-Haze, a novel dataset consisting of 55 pairs of real haze ...
Ancuti, Codruta O., Ancuti, Cosmin, Chang, Jerome, Chang, Yu-Bang, Chao, Ju-Chin, Chen, Peng-Wen, Chen, Siyi, Cherukuri, Venkateswararao, Chuang, Shiue-Yuan, Das, Sourya Dipta, de Dravo, Vincent Whannou, Deng, Qili, Do, Jeonghyeok, Dong, Yu, Dutta, Saikat, Estrada, Dennis, Guo, Tiantong, Hsu, Po-Min, Huang, Ziling, Kim, Munchurl, Kong, Xiangzhen, Lee, David, Li, Xuelu, Liao, Tzu-Yi, Lin, Chang-Hong, Lin, Tsung-Nan, Liu, Jing, Ma, Lizhuang, Mandal, Murari, Mehra, Aryan, Metwaly, Kareem, Monga, Vishal, Narang, Pratik, Ouyang, Bing, Park, Jongmin, Qu, Yanyun, Raghunath, Navaneeth, Su, Tianqi, Sun, Bangyong, Sun, Chung-En, Timofte, Radu, Vasluianu, Florin-Alexandru, Wang, Meiqi, Wang, Zhan-Han, Wu, Haiyan, Wu, Pei-Yuan, Xie, Yuan, Yang, Tsung-Shan, Yu, Mingzhao, Yu, Zhe, Zhao, Xuan, Zhou, Hongyu   +51 more
core   +1 more source

Contrastive Multiscale Transformer for Image Dehazing. [PDF]

Sensors (Basel)
Images obtained in an unfavorable environment may be affected by haze or fog, leading to fuzzy image details, low contrast, and loss of important information. Recently, significant progress has been achieved in the realm of image dehazing, largely due to the adoption of deep learning techniques.
Chen J, Zhao G.
europepmc   +4 more sources

Image Haze Removal Algorithm Based on Nonsubsampled Contourlet Transform

IEEE Access, 2021
In order to avoid the noise diffusion and amplification caused by traditional dehazing algorithms, a single image haze removal algorithm based on nonsubsampled contourlet transform (HRNSCT) is proposed. The HRNSCT removes haze only from the low-frequency
Bowen Zhang, Manli Wang, Xiaobo Shen
doaj   +1 more source

Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing

, 2020
Recently, CNN based end-to-end deep learning methods achieve superiority in Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing.
Das, Sourya Dipta, Dutta, Saikat
core   +1 more source

Polarization Guided Autoregressive Model for Depth Recovery

IEEE Photonics Journal, 2017
The Instant Dehaze method used polarized images to obtain a dehazed image and an estimated depth map of the scene. Haze due to atmospheric absorption and scattering causes degradation in image quality and the estimated depth.
Mohamed Reda, Yongqiang Zhao, Jonathan Cheung-Wai Chan   +2 more
doaj   +1 more source

Guided-Pix2Pix: End-to-End Inference and Refinement Network for Image Dehazing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Haze removal is still an essential prerequisite for image processing and computer vision tasks, and joint inference and refinement of transmission maps remain challenging in the physical scattering model-based haze removal methods.
Libin Jiao, Changmiao Hu, Lianzhi Huo, Ping Tang   +3 more
doaj   +1 more source