Results 31 to 40 of about 3,318 (186)

Deeplearning method for single image dehazing based on HSI colour space

Journal of Measurement Science and Instrumentation, 2021
The traditional single image dehazing algorithm is susceptible to the prior knowledge of hazy image and colour distortion.A new method of deep learning multi-scale convolution neural network based on HSI colour space for single image dehazing is proposed
CHEN Yong, TAO Meifeng, GUO Hongguang
doaj  

Single Image Dehazing via NIN-DehazeNet

IEEE Access, 2019
Single image dehazing has always been a challenging problem in the field of computer vision. Traditional image defogging methods use manual features.
Kangle Yuan, Jianguo Wei, Wenhuan Lu, Naixue Xiong   +3 more
doaj   +1 more source

Dual-Scale Single Image Dehazing via Neural Augmentation

IEEE Transactions on Image Processing, 2022
Model-based single image dehazing algorithms restore haze-free images with sharp edges and rich details for real-world hazy images at the expense of low PSNR and SSIM values for synthetic hazy images. Data-driven ones restore haze-free images with high PSNR and SSIM values for synthetic hazy images but with low contrast, and even some remaining haze ...
Zhengguo Li, Chaobing Zheng, Haiyan Shu, Shiqian Wu   +3 more
openaire   +3 more sources

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

Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing

, 2020
Recently, CNN based end-to-end deep learning methods achieve superiority in Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing.
Das, Sourya Dipta, Dutta, Saikat
core   +1 more source

Learning of Image Dehazing Models for Segmentation Tasks

, 2019
To evaluate their performance, existing dehazing approaches generally rely on distance measures between the generated image and its corresponding ground truth.
berman, chen, fattal, goodfellow, he, johnson, ren, silberman, simonyan, yang   +9 more
core   +1 more source

Single Image Dehazing using CNN

Procedia Computer Science, 2019
Abstract Haze is a natural phenomenon in which the dust, smoke and other particles alter the vision of the sky to reduce the visibility. Hazy images cause various visibility problems for traffic user, tourists everywhere, especially in hilly areas where haze and fog are very common.
Huzaifa Rashid, Nauman Zafar, M Javed Iqbal, Hassan Dawood, Hussain Dawood   +4 more
openaire   +1 more source

Autonomous Single-Image Dehazing: Enhancing Local Texture with Haze Density-Aware Image Blending

Remote Sensing
Single-image dehazing is an ill-posed problem that has attracted a myriad of research efforts. However, virtually all methods proposed thus far assume that input images are already affected by haze. Little effort has been spent on autonomous single-image
Siyeon Han, Dat Ngo, Yeonggyu Choi, Bongsoon Kang   +3 more
doaj   +1 more source

Semantic Single-Image Dehazing

, 2018
Single-image haze-removal is challenging due to limited information contained in one single image. Previous solutions largely rely on handcrafted priors to compensate for this deficiency. Recent convolutional neural network (CNN) models have been used to learn haze-related priors but they ultimately work as advanced image filters.
Cheng, Ziang, You, Shaodi, Ila, Viorela, Li, Hongdong   +3 more
openaire   +2 more sources