Image Super-Resolution Reconstruction Method Using Dual Discriminator Based on Generative Adversarial Networks

2019 ◽  
Vol 56 (23) ◽  
pp. 231010
Author(s):  
袁飘逸 Yuan Piaoyi ◽  
张亚萍 Zhang Yaping
Keyword(s):  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Reconstruction Method ◽  
Adversarial Networks ◽  
Image Super Resolution

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

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.

scholarly journals PET image super-resolution using generative adversarial networks

2020 ◽  
Vol 125 ◽  
pp. 83-91 ◽  
Author(s):  
Tzu-An Song ◽  
Samadrita Roy Chowdhury ◽  
Fan Yang ◽  
Joyita Dutta
Keyword(s):  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Image Super Resolution

scholarly journals Image-Adaptive GAN Based Reconstruction

2020 ◽  
Vol 34 (04) ◽  
pp. 3121-3129 ◽  
Author(s):  
Shady Abu Hussein ◽  
Tom Tirer ◽  
Raja Giryes
Keyword(s):  
Inverse Problems ◽  
Compressed Sensing ◽  
Super Resolution ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Image Super Resolution ◽  
Human Faces

In the recent years, there has been a significant improvement in the quality of samples produced by (deep) generative models such as variational auto-encoders and generative adversarial networks. However, the representation capabilities of these methods still do not capture the full distribution for complex classes of images, such as human faces. This deficiency has been clearly observed in previous works that use pre-trained generative models to solve imaging inverse problems. In this paper, we suggest to mitigate the limited representation capabilities of generators by making them image-adaptive and enforcing compliance of the restoration with the observations via back-projections. We empirically demonstrate the advantages of our proposed approach for image super-resolution and compressed sensing.

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