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Super Resolution

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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

CVPR 2017 tensorflow/models

The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images.

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Image Super-Resolution Using Deep Convolutional Networks

31 Dec 2014nagadomi/waifu2x

We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network.

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Perceptual Losses for Real-Time Style Transfer and Super-Resolution

27 Mar 2016alexjc/neural-enhance

We consider image transformation problems, where an input image is transformed into an output image.

IMAGE SUPER-RESOLUTION NUCLEAR SEGMENTATION STYLE TRANSFER SUPER RESOLUTION

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

1 Sep 2018eriklindernoren/PyTorch-GAN

To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN).

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Deep Image Prior

CVPR 2018 DmitryUlyanov/deep-image-prior

In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning.

IMAGE DENOISING IMAGE INPAINTING IMAGE RESTORATION JPEG COMPRESSION ARTIFACT REDUCTION SUPER RESOLUTION SUPER-RESOLUTION

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

CVPR 2020 adamian98/pulse

We present an algorithm addressing this problem, PULSE (Photo Upsampling via Latent Space Exploration), which generates high-resolution, realistic images at resolutions previously unseen in the literature.

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Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation

23 Nov 2018thunil/TecoGAN

Additionally, we propose a first set of metrics to quantitatively evaluate the accuracy as well as the perceptual quality of the temporal evolution.

IMAGE SUPER-RESOLUTION MOTION COMPENSATION SUPER RESOLUTION VIDEO GENERATION VIDEO SUPER-RESOLUTION

Residual Dense Network for Image Super-Resolution

CVPR 2018 idealo/image-super-resolution

In this paper, we propose a novel residual dense network (RDN) to address this problem in image SR. We fully exploit the hierarchical features from all the convolutional layers.

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