Title :
Sonar Image Denoising via Adaptive Overcomplete Dictionary Based on K-SVD Algorithm
Author :
Di Wu ; Yuxin Zhao ; Lijuan Chen ; Kuimin Wang
Author_Institution :
Coll. of Autom., Harbin Eng. Univ., Harbin, China
Abstract :
In order to remove the noise of sonar image more effectively, the adaptive over complete dictionary based on K-SVD algorithm is carried out in this paper. Given a set of training signals from noisy image, the predefined dictionary is trained so that the new dictionary leads to the best sparse representation for sonar image, but not for the noise. Experiments are provided to demonstrate the performance of the proposed method, as compared with other denoising methods. Results show that this method, which has the capability of adaptation, is particularly appealing in terms of both denoising effect and keeping details, and has improved performance over traditional methods.
Keywords :
image denoising; singular value decomposition; sonar imaging; K-SVD algorithm; adaptive overcomplete dictionary; sonar image denoising; sparse representation; training signals; Dictionaries; Filtering algorithms; Image denoising; Noise; Noise measurement; Noise reduction; Sonar; K-SVD; image denoising; overcomplete dictionary; sonar image;
Conference_Titel :
Business Intelligence and Financial Engineering (BIFE), 2013 Sixth International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4799-4778-2
DOI :
10.1109/BIFE.2013.2