Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution

Medical imaging is widely used in medical diagnosis. The low-resolution image caused by high hardware cost and poor imaging technology leads to the loss of relevant features and even fine texture. Obtaining high-quality medical images plays an important role in disease diagnosis. A surge of deep lea...

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Autores principales:	Yue Yu, Kun She, Jinhua Liu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	medical imaging stationary wavelet transform ghost module attention mechanism Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/21a9b16a6d9449e5a9de1cf6cec39e02
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id	oai:doaj.org-article:21a9b16a6d9449e5a9de1cf6cec39e02
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spelling	oai:doaj.org-article:21a9b16a6d9449e5a9de1cf6cec39e022021-11-25T18:23:51ZWavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution10.3390/mi121114182072-666Xhttps://doaj.org/article/21a9b16a6d9449e5a9de1cf6cec39e022021-11-01T00:00:00Zhttps://www.mdpi.com/2072-666X/12/11/1418https://doaj.org/toc/2072-666XMedical imaging is widely used in medical diagnosis. The low-resolution image caused by high hardware cost and poor imaging technology leads to the loss of relevant features and even fine texture. Obtaining high-quality medical images plays an important role in disease diagnosis. A surge of deep learning approaches has recently demonstrated high-quality reconstruction for medical image super-resolution. In this work, we propose a light-weight wavelet frequency separation attention network for medical image super-resolution (WFSAN). WFSAN is designed with separated-path for wavelet sub-bands to predict the wavelet coefficients, considering that image data characteristics are different in the wavelet domain and spatial domain. In addition, different activation functions are selected to fit the coefficients. Inputs comprise approximate sub-bands and detail sub-bands of low-resolution wavelet coefficients. In the separated-path network, detail sub-bands, which have more sparsity, are trained to enhance high frequency information. An attention extension ghost block is designed to generate the features more efficiently. All results obtained from fusing layers are contracted to reconstruct the approximate and detail wavelet coefficients of the high-resolution image. In the end, the super-resolution results are generated by inverse wavelet transform. Experimental results show that WFSAN has competitive performance against state-of-the-art lightweight medical imaging methods in terms of quality and quantitative metrics.Yue YuKun SheJinhua LiuMDPI AGarticlemedical imagingstationary wavelet transformghost moduleattention mechanismMechanical engineering and machineryTJ1-1570ENMicromachines, Vol 12, Iss 1418, p 1418 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	medical imaging stationary wavelet transform ghost module attention mechanism Mechanical engineering and machinery TJ1-1570
spellingShingle	medical imaging stationary wavelet transform ghost module attention mechanism Mechanical engineering and machinery TJ1-1570 Yue Yu Kun She Jinhua Liu Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution
description	Medical imaging is widely used in medical diagnosis. The low-resolution image caused by high hardware cost and poor imaging technology leads to the loss of relevant features and even fine texture. Obtaining high-quality medical images plays an important role in disease diagnosis. A surge of deep learning approaches has recently demonstrated high-quality reconstruction for medical image super-resolution. In this work, we propose a light-weight wavelet frequency separation attention network for medical image super-resolution (WFSAN). WFSAN is designed with separated-path for wavelet sub-bands to predict the wavelet coefficients, considering that image data characteristics are different in the wavelet domain and spatial domain. In addition, different activation functions are selected to fit the coefficients. Inputs comprise approximate sub-bands and detail sub-bands of low-resolution wavelet coefficients. In the separated-path network, detail sub-bands, which have more sparsity, are trained to enhance high frequency information. An attention extension ghost block is designed to generate the features more efficiently. All results obtained from fusing layers are contracted to reconstruct the approximate and detail wavelet coefficients of the high-resolution image. In the end, the super-resolution results are generated by inverse wavelet transform. Experimental results show that WFSAN has competitive performance against state-of-the-art lightweight medical imaging methods in terms of quality and quantitative metrics.
format	article
author	Yue Yu Kun She Jinhua Liu
author_facet	Yue Yu Kun She Jinhua Liu
author_sort	Yue Yu
title	Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution
title_short	Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution
title_full	Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution
title_fullStr	Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution
title_full_unstemmed	Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution
title_sort	wavelet frequency separation attention network for chest x-ray image super-resolution
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/21a9b16a6d9449e5a9de1cf6cec39e02
work_keys_str_mv	AT yueyu waveletfrequencyseparationattentionnetworkforchestxrayimagesuperresolution AT kunshe waveletfrequencyseparationattentionnetworkforchestxrayimagesuperresolution AT jinhualiu waveletfrequencyseparationattentionnetworkforchestxrayimagesuperresolution
_version_	1718411183705292800

Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution

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