Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018 In this paper, we present an end-to-end network, called Cycle-Dehaze, for single image dehazing problem, which does not require pairs of hazy and corresponding ground truth images for training.
Hazım Kemal Ekenel
exaly +7 more sources
Robust Dehaze Algorithm for Degraded Image of CMOS Image Sensors
Sensors, 2017 The CMOS (Complementary Metal-Oxide-Semiconductor) is a new type of solid image sensor device widely used in object tracking, object recognition, intelligent navigation fields, and so on.
Chen Qu +4 more
doaj +4 more sources
SAM2-Dehaze: Fusing High-Quality Semantic Priors with Convolutions for Single-Image Dehazing [PDF]
SensorsSingle-image dehazing suffers from severe information loss and the under-constraint problem. The lack of high-quality robust priors leads to limited generalization ability of existing dehazing methods in real-world scenarios. To tackle this challenge, we
Sen Li, Jianchao Wang, Zhanqiang Huo
doaj +3 more sources
Dehaze-UNet: A Lightweight Network Based on UNet for Single-Image Dehazing
ElectronicsNumerous extant image dehazing methods based on learning improve performance by increasing the depth or width, the size of the convolution kernel, or using the Transformer structure.
Hao Zhou, Tao Tao
exaly +3 more sources
Dehaze-RetinexGAN: Real-World Image Dehazing via Retinex-Based Generative Adversarial Network
Proceedings of the AAAI Conference on Artificial IntelligenceDeep learning based dehazing networks trained on paired synthetic data have shown impressive performance, but they struggle with significant degradation in generalization ability on real-world hazy scenes.
Xinran Wang, Tian Ye
exaly +3 more sources
Online knowledge distillation network for single image dehazing [PDF]
Scientific Reports, 2022 Single image dehazing, as a key prerequisite of high-level computer vision tasks, catches more and more attentions. Traditional model-based methods recover haze-free images via atmospheric scattering model, which achieve favorable dehazing effect but ...
Yunwei Lan +7 more
doaj +3 more sources
Enhancement of Marine Lantern’s Visibility under High Haze Using AI Camera and Sensor-Based Control System [PDF]
Micromachines, 2023 This thesis describes research to prevent maritime safety accidents by notifying navigational signs when sea fog and haze occur in the marine environment. Artificial intelligence, a camera sensor, an embedded board, and an LED marine lantern were used to
Jehong An +6 more
doaj +2 more sources
Dehaze-attention: enhancing image dehazing with a multi-scale, attention-based deep learning framework [PDF]
Scientific ReportsOver 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 +4 more
doaj +2 more sources
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 +2 more sources
DSNet enables feature fusion and detail restoration for accurate object detection in foggy conditions [PDF]
Scientific ReportsIn real-world scenarios, adverse weather conditions can significantly degrade the performance of deep learning-based object detection models. Specifically, fog reduces visibility, complicating feature extraction and leading to detail loss, which impairs ...
Zhiyong Jing +5 more
doaj +2 more sources