Results 41 to 50 of about 303 (154)
Vehicle license plate recognition for fog‐haze environments
IET Image Processing, 2021 The technique of vehicle license plate recognition can recognize and count the vehicles automatically, and thus many applications regarding the vehicles are greatly facilitated.
Xianli Jin +3 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 +3 more
doaj +1 more source
Sea Ice Floe Segmentation in Close‐Range Optical Imagery Using Active Contour and Foundation Models
Earth and Space Science, Volume 13, Issue 2, February 2026.Abstract The size of sea ice floes in the marginal ice zone (MIZ) is a key factor influencing ice coverage, albedo, wave propagation, and ocean–atmosphere energy exchanges. Floe size can be observed by processing visual‐range imagery from ships, aircraft, or satellites.
Giulio Passerotti +5 more
wiley +1 more source
Artificial Compound Eye for Clear Vision in Harsh Environment
Advanced Functional Materials, Volume 36, Issue 4, 12 January 2026.Artificial compound eye (CE) with exceptional imaging and motion tracking capturing the movement of a spider and swinging thread with a wide field of view. Surface modifications ensure clear vision of alphabetic letters in rain and fog. Images captured in fog with CE remains visible 3 times longer than simple eyes (SE).
Kehinde Kassim +6 more
wiley +1 more source
A novel multiscale cGAN approach for enhanced salient object detection in single haze images
EURASIP Journal on Image and Video ProcessingIn computer vision, image dehazing is a low-level task that employs algorithms to analyze and remove haze from images, resulting in haze-free visuals.
Gayathri Dhara, Ravi Kant Kumar
doaj +1 more source
Enhancing Surveillance Vision with Multi-Layer Deep Learning Representation
MathematicsThis paper aimed to develop a method for generating sand–dust removal and dehazed images utilizing CycleGAN, facilitating object identification on roads under adverse weather conditions such as heavy dust or haze, which severely impair visibility ...
Dong-Min Son, Sung-Hak Lee
doaj +1 more source
RABNet: A Residual Attention‐Based Network for Visibility Restoration in Hazy Images
IET Computers &Digital Techniques, Volume 2026, Issue 1, 2026.Image dehazing enhances visibility and image quality, enabling better decision‐making in critical applications such as surveillance, remote sensing, and autonomous driving. However, single‐image dehazing remains challenging due to the need for smoothness in homogeneous regions, accurate edge preservation, texture fidelity, and artifact‐free ...
Syeda Rabail Zahra +8 more
wiley +1 more source
Electronics Letters, Volume 62, Issue 1, January/December 2026.PhysMamba‑Unroll, a framework that addresses the high computational cost of deploying deep unfolding networks on edge devices by synergistically integrating Mamba's linear complexity with physics‐informed unrolling. ABSTRACT Real‐time adverse weather image restoration on edge devices is hindered by a fundamental tension: Deep unfolding networks (DUNs ...
Yongkang Li +4 more
wiley +1 more source
SINGLE-IMAGE DEHAZING ON AERIAL IMAGERY USING CONVOLUTIONAL NEURAL NETWORKS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 Haze contains floating particles in the air which can result in image quality degradation and visibility reduction in airborne data. Haze removal task has several applications in image enhancement and can improve the performance of automatic image ...
M. Madadikhaljan +5 more
doaj +1 more source
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