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A pan-sharpening method based on NSCT and formulated as compressive sensing problem
In this paper, a pan-sharpening method, using non-subsampled contourlet transform (NSCT) and the theory of compressive sensing (CS), is proposed. The NSCT is used for sparse image representation to perform a multiscale and directional decomposition of source images in order to express their detail and express the sparsity of their high frequency. The CS is used to merge the multispectral (MS) and panchromatic (pan) images from partial random measurements. Two different fusion rules are then applied. The final pan-sharpened image is obtained by inverse NSCT. Experimental results show the efficiency of the proposed method, compared with pan sharpening based on standard NSCT, in terms of visual quality and objective assessment. Moreover, the proposed technique is very effective when the storage and transmission bandwidth are limited.
A pan-sharpening method based on NSCT and formulated as compressive sensing problem
In this paper, a pan-sharpening method, using non-subsampled contourlet transform (NSCT) and the theory of compressive sensing (CS), is proposed. The NSCT is used for sparse image representation to perform a multiscale and directional decomposition of source images in order to express their detail and express the sparsity of their high frequency. The CS is used to merge the multispectral (MS) and panchromatic (pan) images from partial random measurements. Two different fusion rules are then applied. The final pan-sharpened image is obtained by inverse NSCT. Experimental results show the efficiency of the proposed method, compared with pan sharpening based on standard NSCT, in terms of visual quality and objective assessment. Moreover, the proposed technique is very effective when the storage and transmission bandwidth are limited.
A pan-sharpening method based on NSCT and formulated as compressive sensing problem
Mazari, Sarah (author) / Chikr El Mezouar, Miloud / Belloulata, Kamel
2016
Article (Journal)
English
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