Results 51 to 60 of about 11,754,470 (203)
Region Adaptive Single Image Dehazing
, 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
core +1 more source
The Effects of Image Dehazing Methods Using Dehazing Contrast-Enhancement Filters on Image Compression [PDF]
KSII Transactions on Internet and Information Systems, 2016 To obtain well-dehazed images at the receiver while sustaining low bit rates in the transmission pipeline, this paper investigates the effects of image dehazing methods using dehazing contrast-enhancement filters on image compression for surveillance systems.
Liping Wang 0011 +3 more
openaire +1 more source
Large‐scale characterization of horizontal forest structure from remote sensing optical images
Remote Sensing in Ecology and Conservation, Volume 12, Issue 4, Page 611-628, August 2026.Sub‐meter resolution remote sensing data and tree crown segmentation techniques hold promise in offering detailed information that can support the characterization of forest structure from a horizontal perspective, offering new insights in the tree crown structure at scale.
Xin Xu +12 more
wiley +1 more source
Dehazing algorithm for complex environment video images considering visual communication effects
Journal of Radiation Research and Applied SciencesThe communication effect of outdoor computer vision systems is closely related to the dehazing performance of video images. Currently, video image restoration methods in complex environments are still in an urgent development stage.
Yisa Yu, Jianwen Li
doaj +1 more source
Task‐Aligned Haze Removal With Semantic‐Aware Fusion and Contrast Self‐Correction
CAAI Transactions on Intelligence Technology, Volume 11, Issue 4, Page 978-993, August 2026.ABSTRACT Adverse haze conditions introduce complex degradations that obscure scene details and distort structural cues critical for object detection, posing persistent challenges for vision‐based sensing systems. Although existing haze removal methods have achieved notable improvements in visual clarity, their optimisation objectives are often ...
Jinbin Wang +5 more
wiley +1 more source
Nighttime image dehazing based on Retinex and dark channel prior using Taylor series expansion
, 2020 © 2020 Elsevier Inc. Haze removal from nighttime images is more difficult compared with daytime image dehazing due to the uneven illumination, low contrast and severe color distortion.
Liu, H +5 more
core +1 more source
Progressive Colour Equalisation and Detail Refinement for Underwater Image Enhancement
CAAI Transactions on Intelligence Technology, Volume 11, Issue 3, Page 709-725, June 2026.ABSTRACT Underwater image enhancement remains a critical challenge in computational vision due to complex distortions caused by wavelength‐dependent light absorption and scattering. This paper introduces CEDFNet, a novel two‐stage framework that leverages advanced computational intelligence techniques for robust and high‐fidelity underwater image ...
Songbai Liu, Jiacheng Huang
wiley +1 more source
Image and video dehazing by regularized optimization
, 2017 Images and videos captured in hazy weather often yield low contrast and offer limited visibility due to the presence of haze in the atmosphere. Dr Jiaxi studied the image and video dehazing problem.
Jiaxi He (3115425)
core +1 more source
A Nighttime and Daytime Single-Image Dehazing Method
, 2022 In this study, the requirements for image dehazing methods have been put forward, such as a wider range of scenarios in which the methods can be used, faster processing speeds and higher image quality.
Yin Xiang, Yunqing Tang, Guangfeng Chen
core +1 more source
Detection of Targets in Road Scene Images Enhanced Using Conditional GAN-Based Dehazing Model
Applied Sciences, 2023 Object detection is a classic image processing problem. For instance, in autonomous driving applications, targets such as cars and pedestrians are detected in the road scene video. Many image-based object detection methods utilizing hand-crafted features
Tsz-Yeung Chow +2 more
doaj +1 more source