Nighttime image dehazing using color cast removal and dual path multi-scale fusion strategy

2021 ◽  
Vol 16 (4) ◽  
Author(s):  
Bo Wang ◽  
Li Hu ◽  
Bowen Wei ◽  
Zitong Kang ◽  
Chongyi Li
Keyword(s):  
Image Dehazing ◽  
Fusion Strategy ◽  
Multi Scale ◽  
Color Cast ◽  
Nighttime Image

Single nighttime image dehazing based on image decomposition

2021 ◽  
Vol 183 ◽  
pp. 107986
Author(s):  
Yun Liu ◽  
Anzhi Wang ◽  
Hao Zhou ◽  
Pengfei Jia
Keyword(s):  
Image Decomposition ◽  
Image Dehazing ◽  
Nighttime Image

Nighttime image Dehazing with modified models of color transfer and guided image filter

2017 ◽  
Vol 77 (3) ◽  
pp. 3125-3141 ◽  
Author(s):  
Bo Jiang ◽  
Hongqi Meng ◽  
Xiaolei Ma ◽  
Lin Wang ◽  
Yan Zhou ◽  
...  
Keyword(s):  
Color Transfer ◽  
Image Dehazing ◽  
Image Filter ◽  
Guided Image Filter ◽  
Nighttime Image

scholarly journals A Dual-Model Architecture with Grouping-Attention-Fusion for Remote Sensing Scene Classification

2021 ◽  
Vol 13 (3) ◽  
pp. 433
Author(s):  
Junge Shen ◽  
Tong Zhang ◽  
Yichen Wang ◽  
Ruxin Wang ◽  
Qi Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Representation ◽  
Dual Model ◽  
Scene Classification ◽  
Remote Sensing Images ◽  
Single Model ◽  
Fusion Strategy ◽  
Multi Scale ◽  
The Arts ◽  
Scene Representation

Remote sensing images contain complex backgrounds and multi-scale objects, which pose a challenging task for scene classification. The performance is highly dependent on the capacity of the scene representation as well as the discriminability of the classifier. Although multiple models possess better properties than a single model on these aspects, the fusion strategy for these models is a key component to maximize the final accuracy. In this paper, we construct a novel dual-model architecture with a grouping-attention-fusion strategy to improve the performance of scene classification. Specifically, the model employs two different convolutional neural networks (CNNs) for feature extraction, where the grouping-attention-fusion strategy is used to fuse the features of the CNNs in a fine and multi-scale manner. In this way, the resultant feature representation of the scene is enhanced. Moreover, to address the issue of similar appearances between different scenes, we develop a loss function which encourages small intra-class diversities and large inter-class distances. Extensive experiments are conducted on four scene classification datasets include the UCM land-use dataset, the WHU-RS19 dataset, the AID dataset, and the OPTIMAL-31 dataset. The experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-arts.

Single Image Dehazing via Multi-scale Convolutional Neural Networks

2016 ◽  
pp. 154-169 ◽  
Author(s):  
Wenqi Ren ◽  
Si Liu ◽  
Hua Zhang ◽  
Jinshan Pan ◽  
Xiaochun Cao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Single Image ◽  
Image Dehazing ◽  
Multi Scale ◽  
Single Image Dehazing

Multi-scale guided filter and its application in image dehazing

2017 ◽  
Vol 25 (8) ◽  
pp. 2182-2194 ◽  
Author(s):  
武 昆 WU Kun ◽  
韩广良 HAN Guang-liang ◽  
杨 航 YANG Hang ◽  
王宇庆 WANG Yu-qing ◽  
吴笑天 WU Xiao-tian
Keyword(s):  
Guided Filter ◽  
Image Dehazing ◽  
Multi Scale

scholarly journals Image Dehazing Based on (CMTnet) Cascaded Multi-scale Convolutional Neural Networks and Efficient Light Estimation Algorithm

2020 ◽  
Vol 10 (3) ◽  
pp. 1190
Author(s):  
Samia Haouassi ◽  
Di Wu
Keyword(s):  
State Of The Art ◽  
Estimation Algorithm ◽  
Surveillance Systems ◽  
Security Systems ◽  
High Quality ◽  
Image Dehazing ◽  
Multi Scale ◽  
Second Stage ◽  
Computer Vision Applications ◽  
Atmospheric Light

Image dehazing plays a pivotal role in numerous computer vision applications such as object recognition, surveillance systems, and security systems, where it can be considered as an introductory stage. Recently, many proposed learning-based works address this significant task; however, most of them neglect the atmospheric light estimation and fail to produce accurate transmission maps. To address such a problem, in this paper, we propose a two-stage dehazing system. The first stage presents an accurate atmospheric light algorithm labeled “A-Est” that employs hazy image blurriness and quadtree decomposition. Te second stage represents a cascaded multi-scale CNN model called CMT n e t that consists of two subnetworks, one for calculating rough transmission maps (CMCNN t r ) and the other for its refinement (CMCNN t ). Each subnetwork is composed of three-layer D-units (D indicates dense). Experimental analysis and comparisons with state-of-the-art dehazing methods revealed that the proposed system can estimate AL and t efficiently and accurately by achieving high-quality dehazing results and outperforms state-of-the-art comparative methods according to SSIM and MSE values, where our proposed achieves the best scores of both (91% average SSIM and 0.068 average MSE).

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