Rain and snow removal using multi-guided filter and anisotropic gradient in the quaternion framework

2019 ◽  
Author(s):  
Viacheslav V. Voronin ◽  
Evgenii Semenishchev ◽  
Marina Zhdanova ◽  
Roman Sizyakin ◽  
Alexander Zelenskii
Keyword(s):  
Guided Filter ◽  
Snow Removal

Single-Image-Based Rain and Snow Removal Using Multi-guided Filter

2013 ◽  
pp. 258-265 ◽  
Author(s):  
Xianhui Zheng ◽  
Yinghao Liao ◽  
Wei Guo ◽  
Xueyang Fu ◽  
Xinghao Ding
Keyword(s):  
Guided Filter ◽  
Single Image ◽  
Snow Removal

Milestone solution for snow removal problem in Moscow

2019 ◽  
pp. 66-72
Keyword(s):  
New Technologies ◽  
Environmental Safety ◽  
Winter Period ◽  
Special Importance ◽  
Master Plan ◽  
Infrastructure Development ◽  
Snow Removal ◽  
Surface Areas ◽  
The City ◽  
Deicing Agents

The main principle of the strategy for the complex improvement of the functioning of northern cities in winter, including their infrastructure development, is a comprehensive solution of the problem of industrial-scale snow-mass collection, removal and utilization at different areas of urban roadway networks. For its implementation in the capital of Russia, “MosvodokanalNIIproject” JSC developed in 2002 the Snow Removal Master Plan for the city of Moscow. The meteorological conditions in the city, which have changed considerably in the recent years, including the changes in the snow-cover depth and in the road-surface areas to be cleaned, as well as emerging of new technologies for the cleaning of urban streets, yards and sidewalks and some new types of deicing agents, resulted in the necessity to update the above-mentioned Snow Removal Master Plan developed for the city of Moscow. Efficient application of deicing agents is of special importance for its updating in the context of the environmental safety of the city in a winter period. The article considers the results of the implementation of the updated Snow Removal Master Plan and contains some proposals concerning snow removal under the conditions of extreme snowfalls.

Modeling the Snow Deposit Process on the Highways in the Flowvision Software

2020 ◽  
pp. 72-83
Author(s):  
Т. В. Самодурова ◽  
О. В. Гладышева ◽  
Н. Ю. Алимова ◽  
Е. А. Бончева
Keyword(s):  
Hydrodynamic Model ◽  
Geometric Model ◽  
Information Resources ◽  
Road Maintenance ◽  
Snow Removal ◽  
Experimental Section ◽  
Deposit Modeling ◽  
Winter Road ◽  
Highway Embankment ◽  
Winter Road Maintenance

Постановка задачи. Рассмотрена задача моделирования отложения снега во время метелей на автомагистралях с барьерными ограждениями в программе FlowVision . Результаты. В качестве опытного участка рассмотрен участок автомагистрали, проходящий в насыпи. Создана геометрическая модель участка автомагистрали. Обоснованы информационные ресурсы для создания гидродинамической модели обтекания насыпи автомагистрали с барьерными ограждениями снеговетровым потоком во время метелей. Проведено моделирование процесса снегонакопления на опытном участке с использованием программного комплекса FlowVision во время метелей с различными параметрами. Выводы. Сделан вывод о возможности применения программного комплекса FlowVision для совершенствования методики назначения снегозащитных устройств и определения параметров снегоочистки при зимнем содержании автомобильных дорог. Statement of the problem. The problems of snow deposit modeling on the highways with crash barriers during blizzards in the FlowVision was discussed. Results. The highway section passing in the embankment as an experimental section has been considered. The geometric model of the highway section was created. The information resources for designing a hydrodynamic model of a snowflow stream of highway embankment with barriers during blizzard were identified. The modeling of the snow deposit process in the experimental section using the FlowVision software during blizzards with different parameters was carried out. Conclusions. It was concluded that it is possible to use the FlowVision software to improve the methodology for snow protection designing and determining snow removal parameters for winter road maintenance.

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.

A new technique for guided filter based image denoising using modified cuckoo search optimization

2021 ◽  
Vol 176 ◽  
pp. 114884
Author(s):  
Himanshu Singh ◽  
Sethu Venkata Raghavendra Kommuri ◽  
Anil Kumar ◽  
Varun Bajaj
Keyword(s):  
Image Denoising ◽  
Cuckoo Search ◽  
New Technique ◽  
Guided Filter ◽  
Search Optimization ◽  
Cuckoo Search Optimization ◽  
A New Technique
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