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

Image Fusion Method Based on Multi-scale Directional Fast Guided Filter and Convolutional Sparse Representation

2019 ◽  
Vol 12 (3) ◽  
pp. 263-269
Author(s):  
Liu Xian-Hong ◽  
Chen Zhi-Bin
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Filter Bank ◽  
Guided Filter ◽  
Fusion Method ◽  
Edge Preserving ◽  
Directional Information ◽  
Multi Scale ◽  
Directional Filter ◽  
Image Fusion Method

Background: A multi-scale multidirectional image fusion method is proposed, which introduces the Nonsubsampled Directional Filter Bank (NSDFB) into the multi-scale edge-preserving decomposition based on the fast guided filter. Methods: The proposed method has the advantages of preserving edges and extracting directional information simultaneously. In order to get better-fused sub-bands coefficients, a Convolutional Sparse Representation (CSR) based approximation sub-bands fusion rule is introduced and a Pulse Coupled Neural Network (PCNN) based detail sub-bands fusion strategy with New Sum of Modified Laplacian (NSML) to be the external input is also presented simultaneously. Results: Experimental results have demonstrated the superiority of the proposed method over conventional methods in terms of visual effects and objective evaluations. Conclusion: In this paper, combining fast guided filter and nonsubsampled directional filter bank, a multi-scale directional edge-preserving filter image fusion method is proposed. The proposed method has the features of edge-preserving and extracting directional information.

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 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

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).

Single Scale image Dehazing by Multi Scale Fusion

2017 ◽  
Vol 43 (1) ◽  
pp. 30-34
Author(s):  
A Dyanaa ◽  
◽  
Srruthi Thiagarajan Visvanathan ◽  
Varsha Chandran
Keyword(s):  
Image Dehazing ◽  
Multi Scale ◽  
Single Scale

A multi-scale fusion scheme based on haze-relevant features for single image dehazing

2018 ◽  
Vol 283 ◽  
pp. 73-86 ◽  
Author(s):  
Yunan Li ◽  
Qiguang Miao ◽  
Ruyi Liu ◽  
Jianfeng Song ◽  
Yining Quan ◽  
...  
Keyword(s):  
Single Image ◽  
Image Dehazing ◽  
Multi Scale ◽  
Fusion Scheme ◽  
Single Image Dehazing
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