Open Accessremote sensing image striping noise algorithm based on region-weighted double sparse constraint
Author(s) -
Yi Zheng,
Feng Zhou,
Guoyou Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1873/1/012007
Subject(s) - computer science , noise (video) , constraint (computer aided design) , data striping , image (mathematics) , remote sensing , noise reduction , artificial intelligence , computer vision , algorithm , geography , mathematics , geometry , operating system
Infrared remote sensing image has been used in many fields such as land cover crop monitoring, climate monitoring, military strike and early warning. In spaceborne remote sensing imaging system, the stripe is a common noise, seriously affects the researchers’ study of the ground scene. In recent years, the framework that researchers based on different principles use a variety of methods to remove fringe noise in infrared remote sensing images is proposed, such as filtering based method, the method based on statistics, however, most current algorithm still exist the following two problems: some structure or texture was easy to be mistaken as the stripe noise and removed, lost the original information of the image; in some bright or dark areas, it is easy to create artificial traces, adding redundant information and making the denoising result look unnatural. In view of the above two problems, this paper will analyse the causes of the problems and propose a stripe noise removal algorithm based on the region-weighted doublesparse constrained unidirectional variational model, which can effectively reduce the above two phenomena.