Open AccessFuzzy based iterative matting technique for underwater images
Author(s) -
Amin Benish,
Riaz M Mohsin,
Ghafoor Abdul
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12032
Subject(s) - histogram , artificial intelligence , contrast (vision) , pixel , underwater , computer vision , fuzzy logic , alpha (finance) , computer science , image (mathematics) , pattern recognition (psychology) , iterative method , mathematics , algorithm , statistics , geography , construct validity , archaeology , psychometrics
The paper presents an iterative matting technique for extraction of underwater objects from images. The technique adopts histogram division and stretching to obtain multiple images of different contrast levels that exhibit all image details. For each contrast image, alpha matte is produced and is further refined with every iteration. In the end, fuzzy weights are assigned to the alpha mattes obtained at different contrast levels that are combined using weighted average. The resultant alpha matte thus includes more accurate pixels from multiple alpha mattes and generates much refined matte image. The proposed technique is tested, visually and quantitatively, on a manual dataset containing 50 images. The less MSE shows that the proposed technique achieves noticeably higher accuracy as compared with contemporary image matting techniques.