An adaptive SAR image compression method. (August 2017)

• Record Type: Journal Article
• Authors: Ji, XiuXia; Zhang, Gong
• Is Part Of: Computers & electrical engineering. Volume 62(2017)
• Page Start: 473
• Page End: 484
• Publication Date: 2017-08
• Subjects: Image compression -- Discrete wavelet transform -- Adaptive -- Sparse representation; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2016.12.012
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: An adaptive compression method of SAR image is proposed. Represent image with a few coefficients by the over complete dictionary learned directly from image samples rather than a fixed one, the algorithm is adaptive. Use sparse representation model to compress SAR image while suppressing the noise. Abstract: The compression algorithm is very important for SAR image processing. In this paper, an adaptive SAR image compression method is presented. The algorithm consists of three stages: training stage, coding stage and decoding stage. In the training stage, the wavelet dictionaries are constructed according to the training samples. In the coding stage, SAR image is transformed into different frequency subbands in the wavelet domain, preserves the low frequency coefficients, represents the high frequency subbands with sparse coefficients by the dictionaries, and codes all the different frequency coefficients. In the decoding stage, the high frequency coefficients are reconstructed by the dictionaries, and do the invert wavelet transformation on all frequency coefficients to reconstruct the image. The experimental results and performance analysis showed that the proposed method has good results in the compression ratio and PSNR aspects, meanwhile, it can suppress the speckle noise of SAR image. Graphical abstract: