Abstract
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.
Highlights
At present, medical images provide an important basis for disease diagnosis
A total of 10% images were used for valid data set, and the remaining images were used for a test data set, which include normal and abnormal chest images of the two data sets
We propose an effective wavelet frequency separation attention network single-image super-resolution method WFSAN for medical imaging reconstruction, which utilizes features in approximate frequency sub-band coefficients and enhances features in detail frequency sub-band coefficients in the wavelet domain
Summary
Medical images provide an important basis for disease diagnosis. High resolution (HR) medical images provide richer details and better visual quality; they play an important role in experts’ diagnosis. Due to the high cost of hardware equipment and the limitation of imaging technology in a specific situation, obtaining high-resolution medical images by super-resolution has been an important trend [6]. Due to factors such as device configuration, limited scanning time, and body motion, these images with noise and lack of structural information often have low resolution (LR). In such scenarios, super-resolution is preferred by medical professionals to enhance medical images
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