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Missing pixels restoration for remote sensing images using adaptive search window and linear regression

[+] Author Affiliations
Shen-Chuan Tai, Peng-Yu Chen, Chian-Yen Chao

National Cheng Kung University, Department of Electrical Engineering, No. 1, University Road, Tainan City 701, Taiwan

J. Electron. Imaging. 25(4), 043017 (Aug 01, 2016). doi:10.1117/1.JEI.25.4.043017
History: Received March 3, 2016; Accepted July 12, 2016
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Abstract.  The Consultative Committee for Space Data Systems proposed an efficient image compression standard that can do lossless compression (CCSDS-ICS). CCSDS-ICS is the most widely utilized standard for satellite communications. However, the original CCSDS-ICS is weak in terms of error resilience with even a single incorrect bit possibly causing numerous missing pixels. A restoration algorithm based on the neighborhood similar pixel interpolator is proposed to fill in missing pixels. The linear regression model is used to generate the reference image from other panchromatic or multispectral images. Furthermore, an adaptive search window is utilized to sieve out similar pixels from the pixels in the search region defined in the neighborhood similar pixel interpolator. The experimental results show that the proposed methods are capable of reconstructing missing regions with good visual quality.

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© 2016 SPIE and IS&T

Citation

Shen-Chuan Tai ; Peng-Yu Chen and Chian-Yen Chao
"Missing pixels restoration for remote sensing images using adaptive search window and linear regression", J. Electron. Imaging. 25(4), 043017 (Aug 01, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.4.043017


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