Results 61 to 70 of about 303 (154)

Diffusion Models and Its Applications in Image Dehazing: A Survey

IET Image Processing, Volume 20, Issue 1, January/December 2026.
1.This survey represents the first systematic and comprehensive overview of diffusion model‐based image dehazing, aiming to provide a valuable guide for future researchers and stimulate continued progress in this field. 2.We summarize relevant papers along with their corresponding code links and other resources for image dehazing and all‐in‐one image ...
Liangyu Zhu, Yanzhao Su, Zhigao Cui, Yunwei Lan, Nian Wang, Lanqing Zhang, Yanping Cai   +6 more
wiley   +1 more source

See Blue Sky: Deep Image Dehaze Using Paired and Unpaired Training Images

, 2022
The issue of image haze removal has attracted wide attention in recent years. However, most existing haze removal methods cannot restore the scene with clear blue sky, since the color and texture information of the object in the original haze image is ...
Tang, Gaoyang, Zhu, Yingying, Tian, Qi, Zhang, Xiaoyan   +3 more
core  

Image Dehaze Algorithm Based on Improved Atmospheric Scattering Models

IEEE Access
Due to the influence of rainy and foggy weather, obtaining clear images becomes more challenging, often resulting in low visibility, poor contrast, and missing detail information.
Wenqiang Yan, Lei Cui
doaj   +1 more source

SAASU‐UNet++: A Hybrid Swin Transformer–Based Deep Learning Model for Robust Breast Tumour Segmentation in Ultrasound Images

IET Image Processing, Volume 20, Issue 1, January/December 2026.
This study proposes Swin‐attention‐enhanced atrous spatial pyramid pooling (ASPP)‐attention‐squeeze‐and‐excitation (SE) uncertainty‐aware U‐Net++ (SAASU‐UNet++), a novel hybrid deep learning framework for breast tumour segmentation in ultrasound images.
Rahul Singh, Sheifali Gupta, Ahmad Almogren, Salil Bharany, Ateeq Ur Rehman, Belayneh Matebie Taye, Ayman Altameem   +6 more
wiley   +1 more source

GATE-Dehaze: A GAN-attention-transformer ensemble for satellite image dehazing

Turkish Journal of Remote Sensing
The Satellite imagery serves as a cornerstone for various human endeavours including environmental monitoring, urban planning, and disaster management. However, the efficacy of these images heavily relies on their quality, with atmospheric phenomena like
Ishaan Gupta, Yesha Rawal, Shiwani Gupta, Sujata Alegavi   +3 more
doaj   +1 more source

Progressive Back-Traced Dehazing Network Based on Multi-Resolution Recurrent Reconstruction

IEEE Access, 2020
In order to alleviate adverse impacts of haze on high-level vision tasks, image dehazing attracts great attention from computer vision research field in recent years.
Qiaosi Yi, Aiwen Jiang, Juncheng Li, Jianyi Wan, Mingwen Wang   +4 more
doaj   +1 more source

RSDhazer: Residual Shallow‐Deep Network for Efficient Single‐Image Dehazing

IET Image Processing, Volume 20, Issue 1, January/December 2026.
We introduce RSDhazer, a lightweight and efficient end‐to‐end dehazing network designed to handle the complexity of haze removal while maintaining a low computational cost. The proposed RSDhazer comprises three distinct feature extraction blocks that extract shallow and deep features from input images and propagate them through the network without ...
Syeda Rabail Zahra, Farhan Hussain, Tehseen Mazhar, Muhammad Danish Ali, Ghadah Aldehim, Abdul Khader Jilani Saudagar, Muhammad Amir Khan   +6 more
wiley   +1 more source

Unsupervised Dehazing on Real-World Images Using GANs [PDF]

, 2020
Dehazing is an important pre-processing step in almost all computer vision systems deployed in outdoor settings. Existing dehaze methods are either based on heuristic image priors, or on models trained with hazy-clear image pairs of the same scene.
Chadha, Anjali
core  

Single Image Dehazing for a Variety of Haze Scenarios Using Back Projected Pyramid Network [PDF]

, 2020
Learning to dehaze single hazy images, especially using a small training dataset is quite challenging. We propose a novel generative adversarial network architecture for this problem, namely back projected pyramid network (BPPNet), that gives good ...
Ayush Singh, Dilip K. Prasad, Singh, Ayush, Ajay Bhave, Prasad, Dilip K., Bhave, Ajay   +5 more
core   +1 more source

Dehaze-attention: enhancing image dehazing with a multi-scale, attention-based deep learning framework

Scientific Reports
Over the last decade, significant progress has been made in image dehazing, particularly with the advent of deep learning-based methods. However, many of the existing dehazing approaches face critical limitations such as relying on assumptions that fail ...
Hao Huang, G. T. S. Ho, M. W. Geda, Minghao Li, Yuk Ming Tang   +4 more
doaj   +1 more source