Super-Resolution using GANs for Medical Imaging

Gupta, Rohit; Sharma, Anurag; Kumar, Anupam

doi:10.1016/j.procs.2020.06.005

Cited by 33 publications

(19 citation statements)

References 10 publications

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“…A) t-SNE plots with each image coloured according to the noise applied. The colour intensity of each point corresponds to the noise magnitude (in the range [1,30] for Gaussian noise and [0.05, 3] for Poisson noise). All three encoders are capable of separating noise magnitudes, but lose the ability to distinctly separate the two noise classes.…”

Section: Complex Pipeline Resultsmentioning

confidence: 99%

“…This operation enables the exposure of previously-hidden information which can then subsequently be used to improve the performance of any tasks depending on the super-resolved image. SR is thus highly desirable in a vast number of important applications such as medical imaging [1,2], remote sensing [3,4], and in the identification of criminals depicted in Closed-Circuit Television (CCTV) cameras during forensic investigations [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…Poisson sigma/scale values were randomly sampled from the ranges[1,30] and [0.05, 3] respectively. -Compression: We increased the complexity of compression used in previous works by randomly selecting from either JPEG or JM H.264 (version 19) [77] compression at runtime.…”

mentioning

confidence: 99%

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The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks

Aquilina¹,

Ciantar²,

Galea³

et al. 2022

Preprint

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Section: Complex Pipeline Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks

Aquilina¹,

Ciantar²,

Galea³

et al. 2022

Preprint

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“…He et al [24] presented ResNet, as shown in Figure 1a; a residual learning framework that matches a residual mapping rather than the entire mapping. Residual learning has been extensively added to feature extraction modules to strengthen network training capabilities [8,28,38,41]. For traditional residual blocks, batch normalization (BN) generally performs direct normalization operations on each batch characteristic of the incoming feature maps and recovers the original input by operations such as stretching, scaling, and transformation.…”

Section: Residual Blockmentioning

confidence: 99%

“…Compared with CNN-based networks, generative adversarial network (GAN)-based networks focus on detailed texture features, ameliorating adversarial loss and content loss functions, and generating as realistic images as possible [26][27][28][29][30][31][32][33]. GAN consists of a generator and a discriminator.…”

Section: Introductionmentioning

confidence: 99%

Rock CT Image Super-Resolution Using Residual Dual-Channel Attention Generative Adversarial Network

Shan

Liu

et al. 2022

Energies

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Because of its benefits in terms of high speed, non-destructiveness, and three-dimensionality, as well as ease of integration with computer simulation, computed tomography (CT) technology is widely applied in reservoir geology research. However, rock imaging is restricted by the device used as there is not a win–win for both the image receptive field and corresponding resolution. Convolutional neural network-based super-resolution reconstruction has become a hot topic in improving the performance of CT images. With the help of a convolution kernel, it can effectively extract characteristics and ignore disturbance information. The dismal truth is that convolutional neural networks still have numerous issues, particularly unclear texture details. To address these challenges, a generative adversarial network (RDCA-SRGAN) was designed to improve rock CT image resolution using the combination of residual learning and a dual-channel attention mechanism. Specifically, our generator employs residual attention to extract additional features; similarly, the discriminator builds on dual-channel attention and residual learning to distinguish generated contextual information and decrease computational consumption. Quantitative and qualitative analyses demonstrate that the proposed model is superior to earlier advanced frameworks and is capable to constructure visually indistinguishable high-frequency details. The quantitative analysis shows our model contributes the highest value of structural similarity, enriching the more detailed texture information. From the qualitative analysis, in enlarged details of the reconstructed images, the edges of the images generated by the RDCA-SRGAN can be shown to be clearer and sharper. Our model not only performs well in subtle coal cracks but also enriches more dissolved carbonate and carbon minerals. The RDCA-SRGAN has substantially enhanced the reconstructed image resolution and our model has great potential to be used in geomorphological study and exploration.

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