Anisotropic Super Resolution In Prostate Mri Using Super Resolution Generative Adversarial Networks

Age estimation models can be employed in many applications, including soft biometrics, content access control, targeted advertising, and many more. However, as some facial images are taken in unrestrained conditions, the quality relegates, which results in the loss of several essential ageing features. This study investigates how introducing a new layer of data processing based on a super-resolution generative adversarial network (SRGAN) model can influence the accuracy of age estimation by enhancing the quality of both the training and testing samples. Additionally, we introduce a novel convolutional neural network (CNN) classifier to distinguish between several age classes. We train one of our classifiers on a reconstructed version of the original dataset and compare its performance with an identical classifier trained on the original version of the same dataset. Our findings reveal that the classifier which trains on the reconstructed dataset produces better classification accuracy, opening the door for more research into building data-centric machine learning systems.

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Image super-resolution using progressive generative adversarial networks for medical image analysis

Computerized Medical Imaging and Graphics ◽

10.1016/j.compmedimag.2018.10.005 ◽

2019 ◽

Vol 71 ◽

pp. 30-39 ◽

Cited By ~ 35

Author(s):

Dwarikanath Mahapatra ◽

Behzad Bozorgtabar ◽

Rahil Garnavi

Keyword(s):

Image Analysis ◽

Medical Image ◽

Medical Image Analysis ◽

Super Resolution ◽

Generative Adversarial Networks ◽

Adversarial Networks ◽

Image Super Resolution

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Applying Holo360 Video and Image Super-Resolution Generative Adversarial Networks to Virtual Reality Immersion

Human-Computer Interaction. Design and User Experience - Lecture Notes in Computer Science ◽

10.1007/978-3-030-49059-1_42 ◽

2020 ◽

pp. 569-584

Author(s):

Chia-Hui Feng ◽

Yu-Hsiu Hung ◽

Chao-Kuang Yang ◽

Liang-Chi Chen ◽

Wen-Cheng Hsu ◽

...

Keyword(s):

Virtual Reality ◽

Super Resolution ◽

Generative Adversarial Networks ◽

Adversarial Networks ◽

Image Super Resolution

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TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3456726 ◽

2021 ◽

Vol 12 (6) ◽

pp. 1-20

Author(s):

Fayaz Ali Dharejo ◽

Farah Deeba ◽

Yuanchun Zhou ◽

Bhagwan Das ◽

Munsif Ali Jatoi ◽

...

Keyword(s):

Remote Sensing ◽

Super Resolution ◽

Generative Adversarial Networks ◽

Single Image ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Image Super Resolution ◽

Spatio Temporal ◽

Single Image Super Resolution

Single Image Super-resolution (SISR) produces high-resolution images with fine spatial resolutions from a remotely sensed image with low spatial resolution. Recently, deep learning and generative adversarial networks (GANs) have made breakthroughs for the challenging task of single image super-resolution (SISR) . However, the generated image still suffers from undesirable artifacts such as the absence of texture-feature representation and high-frequency information. We propose a frequency domain-based spatio-temporal remote sensing single image super-resolution technique to reconstruct the HR image combined with generative adversarial networks (GANs) on various frequency bands (TWIST-GAN). We have introduced a new method incorporating Wavelet Transform (WT) characteristics and transferred generative adversarial network. The LR image has been split into various frequency bands by using the WT, whereas the transfer generative adversarial network predicts high-frequency components via a proposed architecture. Finally, the inverse transfer of wavelets produces a reconstructed image with super-resolution. The model is first trained on an external DIV2 K dataset and validated with the UC Merced Landsat remote sensing dataset and Set14 with each image size of 256 × 256. Following that, transferred GANs are used to process spatio-temporal remote sensing images in order to minimize computation cost differences and improve texture information. The findings are compared qualitatively and qualitatively with the current state-of-art approaches. In addition, we saved about 43% of the GPU memory during training and accelerated the execution of our simplified version by eliminating batch normalization layers.

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