Results 41 to 50 of about 16,734,778 (159)

Image dehazing using two‐dimensional canonical correlation analysis

IET Computer Vision, 2015
Image dehazing is an important issue that interests both image processing and computer vision. In this study, image dehazing is modelled as an example‐based learning problem, and a novel dehazing algorithm using two‐dimensional (2D) canonical correlation
Liqian Wang, Liang Xiao, Zhihui Wei
doaj   +1 more source

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

Dual Multi-Scale Dehazing Network

IEEE Access, 2023
Single-image haze removal is a challenging ill-posed problem. Recently, methods based on training on synthetic data have achieved good dehazing results. However, we note that these methods can be further improved.
Shengdong Zhang, Xiaoqin Zhang, Linlin Shen   +2 more
doaj   +1 more source

Delving Deeper Into Image Dehazing: A Survey

IEEE Access, 2023
Images captured under foggy or hazy weather conditions are affected by the scattering of atmospheric particles, resulting in decreased contrast and color variation, thereby limiting their practical applications.
Guohou Li, Jia Li, Gongchao Chen, Zhibin Wang, Songlin Jin, Chang Ding, Weidong Zhang   +6 more
doaj   +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, Martin Brandt, Xiaowei Tong, Maurice Mugabowindekwe, Yuemin Yue, Sizhuo Li, Qiue Xu, Siyu Liu, Florian Reiner, Kelin Wang, Zhengchao Chen, Yongqing Bai, Rasmus Fensholt   +12 more
wiley   +1 more source

Variational Single Image Dehazing for Enhanced Visualization

, 2020
In this paper, we investigate the challenging task of removing haze from a single natural image. The analysis on the haze formation model shows that the atmospheric veil has much less relevance to chrominance than luminance, which motivates us to neglect
Wang, Tingting, Wang, Yang, Fang, Faming, Zhang, Guixu, Zeng, Tieyong   +4 more
core   +1 more source

A multimodal feature fusion image dehazing method with scene depth prior

IET Image Processing, 2023
Current dehazing networks usually only learn haze features in a single‐image colour space and often suffer from uneven dehazing, colour, and edge degradation when confronted with different scales of ground objects in the depth space of the scene.
Zhang Zhengpeng, Cheng Yan, Zhang Shuai, Bu Lijing, Deng Mingjun   +4 more
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, Aiping Yang, Guosong Jiang, Wenlong Yu, Dongwei Ren, Qinghua Hu   +5 more
wiley   +1 more source

Towards Domain Invariant Single Image Dehazing

Proceedings of the AAAI Conference on Artificial Intelligence, 2021
Presence of haze in images obscures underlying information, which is undesirable in applications requiring accurate environment information. To recover such an image, a dehazing algorithm should localize and recover affected regions while ensuring consistency between recovered and its neighboring regions.
Pranjay Shyam, Kuk-Jin Yoon, Kyung-Soo Kim 0001   +2 more
openaire   +2 more sources

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