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

Zhang, Shengxiang; Liang, Gaobo; Pan, Shuwan; Zheng, Lixin

doi:10.1109/access.2018.2871626

Cited by 78 publications

(55 citation statements)

References 26 publications

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“…• SWT wavelet decomposition is utilized in our proposed method to evaluate the wavelet coefficients. deep learning networks [29]. Still, we train the network on wavelet domain images instead of residuals.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…After analyzing model SRCNN (9-5-5), the feature map is achieved better with the second layer having the configuration of 5 * 5 convolution kernel. In the proposed method, a 5 * 5 convolution kernel is replaced by this mini grid-network to achieve the same results much faster as in [29]. To achieve the greater susceptibility, large convolution kernels will be used with increased numbers of parameters, but it also increases the number of calculations.…”

Section: B Mini-grid-networkmentioning

confidence: 99%

“…The deep network consists of multiple layers, each parameter of each layer is utilized for feature learning purpose, as the layers increase the feature learning rate is also increased. Additionally, mini-grid-network is added for exploiting the speed of the network due to its good quick performance on the network [29]. The proposed model comprised of three layers, conv1, and con3 comprised of 32 kernel size with 3 * 3 conv size with Tanh activation function, while conv2 is named as mini-grid-network.…”

Section: Hidden Layersmentioning

confidence: 99%

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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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Section: Proposed Methodsmentioning

confidence: 99%

Section: B Mini-grid-networkmentioning

confidence: 99%

Section: Hidden Layersmentioning

confidence: 99%

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Wavelet-Based Enhanced Medical Image Super Resolution

et al. 2020

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“…To enhance the quality of high definition resolution for images is much important for the identification of macula fovea and also for the analysis of fundus images. In Zhang et al [135], made a contribution in the area of retinopathy to improve the resolution of images. In the reported article, the author introduced the super-resolution technique on the basis of deep learning, to improve the quality of low leveled resolution images.…”

Section: ) Optic Disc and Optic Cup Segmentation Techniquesmentioning

confidence: 99%

Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics

et al. 2020

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Diabetic retinopathy (DR) is a fast-spreading disease across the globe, which is caused by diabetes. The DR may lead the diabetic patients to complete vision loss. In this scenario, early identification of DR is more essential to recover the eyesight and provide help for timely treatment. The detection of DR can be manually performed by ophthalmologists and can also be done by an automated system. In the manual system, analysis and explanation of retinal fundus images need ophthalmologists, which is a timeconsuming and very expensive task, but in the automated system, artificial intelligence is used to perform an imperative role in the area of ophthalmology and specifically in the early detection of diabetic retinopathy over the traditional detection approaches. Recently, numerous advanced studies related to the identification of DR have been reported. This paper presents a detailed review of the detection of DR with three major aspects; retinal datasets, DR detection methods, and performance evaluation metrics. Furthermore, this study also covers the author's observations and provides future directions in the field of diabetic retinopathy to overcome the research challenges for the research community. INDEX TERMS Artificial intelligence, deep learning, diabetic retinopathy, fundus images, machine learning, ophthalmology.

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“…As one of them, single image super-resolution (SISR) has attracted considerable attention for both academic and commercial applications. SISR has been widely used in many fields, such as medical diagnosis [17], remote sensing [18], synthetic aperture radar (SAR) [19], [20], and face completion [21], and has achieved remarkable results. Consequently, SISR is a potentially suitable choice to achieve improvement in interference image resolution.…”

Section: Introductionmentioning

confidence: 99%

White-Light Interference Microscopy Image Super-Resolution Using Generative Adversarial Networks

Zhang

et al. 2020

IEEE Access

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To reduce external disturbances and achieve high vertical resolution, the scanning time for white-light interference microscopy is very short. Because capturing high-resolution (HR) images is time consuming, low-resolution (LR) images are acquired instead. However, HR images are more desirable because they contain more details. To ensure high vertical resolution and high image resolution, one feasible solution is to process the scanned LR images to HR images by single image super-resolution (SISR). In this paper, an interference image super-resolution (IISR) model based on a generative adversarial network (GAN) is proposed. The generator is based on the enhanced super-resolution generative adversarial network (ESRGAN) architecture. With the aim of acquiring more realistic images, the discriminator network is designed using a modified DenseNet architecture, in which the pooling layers are replaced with dilated convolutional layers. The perceptual loss is optimized, and the content loss is upgraded to a continuously differentiable piecewise function. Various microscopy images are tested, including images with and without interference fringes. The IISR model has been proven to restore LR images to HR images. The comparative experiments prove that the proposed model achieves better visual quality than other models, preserving more realistic details. INDEX TERMS White-light interference microscopy, single image super-resolution, generative adversarial network, visual quality.

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

Cited by 78 publications

References 26 publications

Wavelet-Based Enhanced Medical Image Super Resolution

Wavelet-Based Enhanced Medical Image Super Resolution

Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics

White-Light Interference Microscopy Image Super-Resolution Using Generative Adversarial Networks

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