Results 11 to 20 of about 303 (154)
CoRR, 2022 In this paper, we introduce a new computer vision task called nighttime dehaze-enhancement. This task aims to jointly perform dehazing and lightness enhancement. Our task fundamentally differs from nighttime dehazing -- our goal is to jointly dehaze and enhance scenes, while nighttime dehazing aims to dehaze scenes under a nighttime setting.
Harshan Baskar +9 more
openaire +4 more sources
Single Image Dehazing Using End-to-End Deep-Dehaze Network [PDF]
Electronics, 2020 Haze is a natural distortion to the real-life images due to the specific weather conditions. This distortion limits the perceptual fidelity, as well as information integrity, of a given image. Image dehazing for the observed images is a complicated task because of its ill-posed nature.
Masud An-Nur Islam Fahim, Ho Yub Jung
core +4 more sources
Research on Multi-Scale Feature Fusion Dehazing Network Based on Feature Differences [PDF]
Jisuanji gongchengHaze, formed by the accumulation and concentration of atmospheric pollutants under meteorological conditions, such as temperature inversion, severely limits visibility.
LIU Yanhong, YANG Qiuxiang, HU Shuai
doaj +3 more sources
Nighttime Image Dehazing by Render
Journal of Imaging, 2023 Nighttime image dehazing presents unique challenges due to the unevenly distributed haze caused by the color change of artificial light sources. This results in multiple interferences, including atmospheric light, glow, and direct light, which make the ...
Zheyan Jin +3 more
doaj +4 more sources
Progressive Pruning of Light Dehaze Networks for Static Scenes
Applied SciencesThis paper introduces an progressive pruning method for Light DeHaze Networks, focusing on a static scene captured by a fixed camera environments. We develop a progressive pruning algorithm that aims to reduce computational complexity while maintaining ...
Byeongseon Park +3 more
doaj +2 more sources
Advanced Intelligent SystemsImage dehazing, a fundamental computer vision task, is crucial for the stability of multiple downstream systems. Traditional methods have limited generalization, while deep learning methods face bottlenecks like error accumulation and poor adaptability ...
Ruipeng Wang +4 more
doaj +2 more sources
Efficient Dehazing with Recursive Gated Convolution in U-Net: A Novel Approach for Image Dehazing
Journal of Imaging, 2023 Image dehazing, a fundamental problem in computer vision, involves the recovery of clear visual cues from images marred by haze. Over recent years, deploying deep learning paradigms has spurred significant strides in image dehazing tasks.
Zhibo Wang +3 more
doaj +2 more sources
SAM2-Dehaze: Fusing High-Quality Semantic Priors with Convolutions for Single-Image Dehazing
SensorsSingle-image dehazing suffers from severe information loss and the under-constraint problem. The lack of high-quality robust priors leads to limited generalization ability of existing dehazing methods in real-world scenarios. To tackle this challenge, we
Sen Li, Jianchao Wang, Zhanqiang Huo
doaj +2 more sources
Unsupervised Dark-Channel Attention-Guided CycleGAN for Single-Image Dehazing
Sensors, 2020 In this paper, we propose a new unsupervised attention-based cycle generative adversarial network to solve the problem of single-image dehazing. The proposed method adds an attention mechanism that can dehaze different areas on the basis of the previous ...
Jiahao Chen +3 more
doaj +2 more sources
Applied Computational Intelligence and Soft ComputingThe invention of infrastructure facilities in intelligent transportation systems is skyrocketing every day. The improvements in such things have many pros and cons.
K. P. Risha +3 more
doaj +2 more sources