Compressive sampling based on frequency saliency for remote sensing imaging

Abstract In saliency-based compressive sampling (CS) for remote sensing image signals, the saliency information of images is used to allocate more sensing resources to salient regions than to non-salient regions. However, the pulsed cosine transform method can generate large errors in the calculatio...

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Autores principales:	Jin Li, Zilong Liu, Fengdeng Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8dfe2df180f14a379f23006b74aa9aae
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spelling	oai:doaj.org-article:8dfe2df180f14a379f23006b74aa9aae2021-12-02T15:06:00ZCompressive sampling based on frequency saliency for remote sensing imaging10.1038/s41598-017-06834-42045-2322https://doaj.org/article/8dfe2df180f14a379f23006b74aa9aae2017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-06834-4https://doaj.org/toc/2045-2322Abstract In saliency-based compressive sampling (CS) for remote sensing image signals, the saliency information of images is used to allocate more sensing resources to salient regions than to non-salient regions. However, the pulsed cosine transform method can generate large errors in the calculation of saliency information because it uses only the signs of the coefficients of the discrete cosine transform for low-resolution images. In addition, the reconstructed images can exhibit blocking effects because blocks are used as the processing units in CS. In this work, we propose a post-transform frequency saliency CS method that utilizes transformed post-wavelet coefficients to calculate the frequency saliency information of images in the post-wavelet domain. Specifically, the wavelet coefficients are treated as the pixels of a block-wise megapixel sensor. Experiments indicate that the proposed method yields better-quality images and outperforms conventional saliency-based methods in three aspects: peak signal-to-noise ratio, mean structural similarity index, and visual information fidelity.Jin LiZilong LiuFengdeng LiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jin Li Zilong Liu Fengdeng Liu Compressive sampling based on frequency saliency for remote sensing imaging
description	Abstract In saliency-based compressive sampling (CS) for remote sensing image signals, the saliency information of images is used to allocate more sensing resources to salient regions than to non-salient regions. However, the pulsed cosine transform method can generate large errors in the calculation of saliency information because it uses only the signs of the coefficients of the discrete cosine transform for low-resolution images. In addition, the reconstructed images can exhibit blocking effects because blocks are used as the processing units in CS. In this work, we propose a post-transform frequency saliency CS method that utilizes transformed post-wavelet coefficients to calculate the frequency saliency information of images in the post-wavelet domain. Specifically, the wavelet coefficients are treated as the pixels of a block-wise megapixel sensor. Experiments indicate that the proposed method yields better-quality images and outperforms conventional saliency-based methods in three aspects: peak signal-to-noise ratio, mean structural similarity index, and visual information fidelity.
format	article
author	Jin Li Zilong Liu Fengdeng Liu
author_facet	Jin Li Zilong Liu Fengdeng Liu
author_sort	Jin Li
title	Compressive sampling based on frequency saliency for remote sensing imaging
title_short	Compressive sampling based on frequency saliency for remote sensing imaging
title_full	Compressive sampling based on frequency saliency for remote sensing imaging
title_fullStr	Compressive sampling based on frequency saliency for remote sensing imaging
title_full_unstemmed	Compressive sampling based on frequency saliency for remote sensing imaging
title_sort	compressive sampling based on frequency saliency for remote sensing imaging
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/8dfe2df180f14a379f23006b74aa9aae
work_keys_str_mv	AT jinli compressivesamplingbasedonfrequencysaliencyforremotesensingimaging AT zilongliu compressivesamplingbasedonfrequencysaliencyforremotesensingimaging AT fengdengliu compressivesamplingbasedonfrequencysaliencyforremotesensingimaging
_version_	1718388657172250624

Compressive sampling based on frequency saliency for remote sensing imaging

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