A deep convolutional network for medical image super-resolution

Gao, Yunxing; Li, Hengjian; Dong, Jiwen; Feng, Guang

doi:10.1109/cac.2017.8243724

Cited by 25 publications

(11 citation statements)

References 14 publications

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“…The possibilities and reliability of images recreated using deep learning methods are proven by the fact that they are utilized for the improvement of the resolution of medical imaging [179]- [181]. An example is a solution proposed by Zamzmi et al [182] that enables to enhance the resolution of X-ray images.…”

Section: Table VI Comparison Of the Psnr [Db]/ssim Metrics Of The Mos...mentioning

confidence: 99%

Using Super-Resolution Algorithms for Small Satellite Imagery: A Systematic Review

Karwowska

Wierzbicki

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: Table VI Comparison Of the Psnr [Db]/ssim Metrics Of The Mos...mentioning

confidence: 99%

Using Super-Resolution Algorithms for Small Satellite Imagery: A Systematic Review

Karwowska

Wierzbicki

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…However, these images are usually in low quality and lack of internal information. Due to hardware and current imaging technology limitations, medical professionals and researchers prefer image super-resolution processing technology for medical diagnosis [4]. The Single Image Super-Resolution (SISR) problem is considered very complex in theory because the number of unknown variables in the High-Resolution (HR) image is better than in the Low-Resolution (LR) image.…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by the three-layer network SRCNN [8], Gao et al [4] use a deep convolution network to achieve super-resolution of a single image on a medical data set. The treatment diagnosis can be further improved by reconstructing these data sets.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-Based Enhanced Medical Image Super Resolution

et al. 2020

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Low-resolution medical images can seriously interfere with the medical diagnosis, and poor image quality can lead to loss of detailed information. Therefore, improving the quality of medical images and accelerating the reconstruction is of particular importance for diagnosis. To solve this problem, we propose a wavelet-based mini-grid network medical image super-resolution (WMSR) method, which is similar to the three-layer hidden-layer-based super-resolution convolutional neural network (SRCNN) method. Due to the amplification characteristics of wavelets, a stationary wavelet transform (SWT) is used instead of a discrete wavelet transform (DWT). Also, due to the nature of redundant (scale-by-scale) wavelets, it is possible to retain additional information about the image and restore high-resolution images in detail. For a large amount of training data, wavelet sub-band images, including approximation and frequency subbands are combined into a predefined full-scale factor. The mapping between the wavelet sub-band image and its approximate image is then determined. In order to ensure the reproducibility of the image, a method of adding a sub-pixel layer is proposed to realize the hidden layer, and replacing the small mini-grid-network on the hidden layer is of considerable significance to speed up the image recovery speed. Experimental results on the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) show that the model has better performance.

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“…However these images have low resolution, inherent noise, and lack of structural information. Due to hardware devices and existing imaging technology limitations, image super-resolution processing is favored by medical experts and researchers for its advantages of being intuitionistic, noninvasive, convenient and secure [4].…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile it is not enough to express the characteristics of object. In 2017, Gao et al [4] utilized a deep convolutional network for medical image super-resolution that is an improved SRCNN algorithm. The reconstructed CT images can clearly provide an important reference for clinicians to make the correct treatment decisions.…”

Section: Introductionmentioning

confidence: 99%

A Fast Medical Image Super Resolution Method Based on Deep Learning Network

et al. 2019

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Low-resolution medical images can hamper medical diagnosis seriously, especially in the analysis of retina images and specifically for the detection of macula fovea. Therefore, improving the quality of medical images and speeding up their reconstruction is particularly important for expert diagnosis. To deal with this engineering problem, our paper presents a fast medical image super-resolution (FMISR) method whereby the three hidden layers to complete feature extraction is as same as the super resolution convolution neural network. It is important that a well-designed deep learning network processes images in the low resolution instead of the high-resolution space and enables the super-resolution reconstruction to be more efficient. Sub-pixel convolution layer addition and mini-network substitution in the hidden layers are critical for improving the image reconstruction speed. While the hidden layers are proposed for ensuring reconstruction quality, our FMISR framework performs significantly faster and produces a higher resolution images. As such, the technique underlying this framework presents a high potential in retinal macular examination as it provides a good platform for the segmentation of retinal images. INDEX TERMS Super resolution, medical imaging, deep learning, medical diagnosis.

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A deep convolutional network for medical image super-resolution

Cited by 25 publications

References 14 publications

Using Super-Resolution Algorithms for Small Satellite Imagery: A Systematic Review

Using Super-Resolution Algorithms for Small Satellite Imagery: A Systematic Review

Wavelet-Based Enhanced Medical Image Super Resolution

A Fast Medical Image Super Resolution Method Based on Deep Learning Network

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