Paper
24 September 2019 Rain and snow removal using multi-guided filter and anisotropic gradient in the quaternion framework
Author Affiliations +
Abstract
In many cases the rain and snow on an image significantly degrade the effectiveness of any computer vision algorithm, such as object recognition, tracking, retrieving and so on. The automated detection and removing such degradations in a color image is still a challenging task. This paper presents a new rain and snow removal method using low- and highfrequency parts of a single image. For this purpose, we use a color image multi-guided filter and anisotropic gradient in Hamiltonian quaternions. The quaternion framework is used to represent a color image to take into account all three channels simultaneously when inpainting the RGB image. Our results show that it has good performance in rain removal and snow removal.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
V. Voronin, E. Semenishchev, M. Zhdanova, R. Sizyakin, and A. Zelenskii "Rain and snow removal using multi-guided filter and anisotropic gradient in the quaternion framework", Proc. SPIE 11169, Artificial Intelligence and Machine Learning in Defense Applications, 111690S (24 September 2019); https://doi.org/10.1117/12.2534744
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Detection and tracking algorithms

Evolutionary algorithms

RGB color model

Computer vision technology

Convolution

Machine vision

Neural networks

RELATED CONTENT

Simple and effective multi-scale fusion strategy
Proceedings of SPIE (November 23 2022)
Real-time detection of abandoned bags using CNN
Proceedings of SPIE (June 26 2017)
ProNet an accurate and light weight CNN model for...
Proceedings of SPIE (July 24 2018)
Object Recognition by a Hopfield Neural Network
Proceedings of SPIE (March 01 1990)

Back to Top