Results 61 to 70 of about 16,734,778 (159)
Aerial Image Dehazing Using Reinforcement Learning
, 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 +3 more
core +1 more source
Single Image Dehazing Algorithm Based on Multi-scale Image Fusion [PDF]
Jisuanji gongcheng, 2016 To effectively improve the quality of the degraded image in foggy days,this paper proposes a dehazing algorithm for single image based on multi-scale image fusion.After the image is converted to gradient domain,each scale value is calculated by means of ...
PAN Lei,ZHENG Yijun
doaj +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
Depth-agnostic Single Image Dehazing
CoRRSingle image dehazing is a challenging ill-posed problem. Existing datasets for training deep learning-based methods can be generated by hand-crafted or synthetic schemes. However, the former often suffers from small scales, while the latter forces models to learn scene depth instead of haze distribution, decreasing their dehazing ability.
Honglei Xu, Yan Shu, Shaohui Liu
openaire +2 more sources
Model-Based Single Image Deep Dehazing
CoRR, 2021 Model-based single image dehazing algorithms restore images with sharp edges and rich details at the expense of low PSNR values. Data-driven ones restore images with high PSNR values but with low contrast, and even some remaining haze. In this paper, a novel single image dehazing algorithm is introduced by fusing model-based and data-driven approaches.
Zhengguo Li +3 more
openaire +3 more sources
IET Image Processing, Volume 20, Issue 1, January/December 2026.This paper proposes a novel image enhancement method, WCTE, which integrates Haar wavelet transform and adaptive CLAHE to improve the visibility of low‐contrast tablet images. Combined with the YOLOv11 model, this approach significantly boosts defect detection accuracy, especially for half‐grain and paste tabtal.
Zimei Tu +3 more
wiley +1 more source
Improved Single Image Dehazing using Geometry
, 2009 Images captured in foggy weather conditions exhibit losses in quality which are dependent on distance. If the depth and atmospheric conditions are known, one can enhance the images (to some degree) by compensating for the effects of the fog.
Hartley, Richard +3 more
core +1 more source
A Unified Variational Model for Single Image Dehazing
IEEE Access, 2019 Haze is a common weather phenomenon, which hinders many outdoor computer vision applications such as outdoor surveillance, navigation control, vehicle driving, and so on.
Yun Liu +4 more
doaj +1 more source
Progressive Knowledge Distillation for Edge‐Deployable Solder Joint Segmentation
IET Image Processing, Volume 20, Issue 1, January/December 2026.Solder‐Yolo is a lightweight deep learning model based on YOLOv8‐seg, designed for high‐precision solder joint inspection in FPC ribbon cables. It incorporates model pruning, knowledge distillation and a hierarchical context attention module to achieve 96.7% precision and 91.3% mAP while maintaining high inference speed.
Kunhong Li +4 more
wiley +1 more source
Image dehazing technique based on polarimetric spectral analysis
, 2016 Image took under hazy weather suffers from poor contrast and resolution, the haze particles will attenuate the light reflected by the targets and add unwanted scattering light.
Xia, Pu, Liu, Xuebin
core +1 more source