Results 1 to 10 of about 81 (67)

ED-Dehaze Net: Encoder and Decoder Dehaze Network.

International Journal of Interactive Multimedia and Artificial Intelligence, 2022
The presence of haze will significantly reduce the quality of images, such as resulting in lower contrast and blurry details. This paper proposes a novel end-to-end dehazing method, called Encoder and Decoder Dehaze Network (ED-Dehaze Net), which ...
Hongqi Zhang, Yixiong Wei, Hongqiao Zhou, Qianhao Wu   +3 more
exaly   +3 more sources

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.
Ping Sui, Chen Qu
exaly   +3 more sources

Deep Generative Models for Automated Dehazing Remote Sensing Satellite Images [PDF]

E3S Web of Conferences, 2023
Remote Sensing (RS) is the process of observing and measuring the physical features of an area from a distance by monitoring its reflected and emitted radiation, usually from a satellite or aircraft.
Poornima E., Mohit Suryadevara, Cheresh Reddy Kunduru, Hemchandra Vallepu, Chandramauli Awadhesh, Kondal Rao Peram   +5 more
doaj   +1 more source

Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder

Applied Sciences, 2021
Removing haze or rain is one of the difficult problems in computer vision applications. On real-world road images, haze and rain often occur together, but traditional methods cannot solve this imaging problem.
Dong Hwan Kim, Woo Jin Ahn, Myo Taeg Lim, Tae Koo Kang, Dong Won Kim   +4 more
doaj   +1 more source

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

Aerial Image Dehazing Using Reinforcement Learning

Remote Sensing, 2022
Aerial observation is usually affected by the Earth’s atmosphere, especially when haze exists. Deep reinforcement learning was used in this study for dehazing.
Jing Yu, Deying Liang, Bo Hang, Hongtao Gao   +3 more
doaj   +1 more source

Enhance Low Visibility Image Using Haze-Removal Framework

IEEE Access, 2023
We proposed a novel image enhancement framework to raise the visibility of the image’s content. Our primary concern is eliminating haze-like effects and simultaneously increasing images’ brightness.
Ping Juei Liu
doaj   +1 more source

Enhanced CycleGAN Network with Adaptive Dark Channel Prior for Unpaired Single-Image Dehazing

Entropy, 2023
Unpaired single-image dehazing has become a challenging research hotspot due to its wide application in modern transportation, remote sensing, and intelligent surveillance, among other applications. Recently, CycleGAN-based approaches have been popularly
Yijun Xu, Hanzhi Zhang, Fuliang He, Jiachi Guo, Zichen Wang   +4 more
doaj   +1 more source

Enhancement of Marine Lantern’s Visibility under High Haze Using AI Camera and Sensor-Based Control System

Micromachines, 2023
This thesis describes research to prevent maritime safety accidents by notifying navigational signs when sea fog and haze occur in the marine environment. Artificial intelligence, a camera sensor, an embedded board, and an LED marine lantern were used to
Jehong An, Kwonwook Son, Kwanghyun Jung, Sangyoo Kim, Yoonchul Lee, Sangbin Song, Jaeyoung Joo   +6 more
doaj   +1 more source

New approach to dehaze single nighttime image

Xibei Gongye Daxue Xuebao, 2021
This paper focus on the dehazing of a single image captured at nighttime. The current state-of-the-art nighttime dehazing approaches usually suffer from the color shift problem due to the fact that the assumptions enforced underdaytime cannot get applied

doaj   +1 more source