Super resolution is the task of taking an input of a low resolution (LR) and upscaling it to that of a high resolution.
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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.
#8 best model for Image Super-Resolution on BSD100 - 4x upscaling
We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network.
#2 best model for Video Super-Resolution on Xiph HD - 4x upscaling
This means that the super-resolution (SR) operation is performed in HR space.
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).
We consider image transformation problems, where an input image is transformed into an output image.
#33 best model for Image Super-Resolution on BSD100 - 4x upscaling
The feed-forward architectures of recently proposed deep super-resolution networks learn representations of low-resolution inputs, and the non-linear mapping from those to high-resolution output.
#4 best model for Image Super-Resolution on BSD100 - 4x upscaling
Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN).
#4 best model for Image Super-Resolution on Urban100 - 4x upscaling
Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality.
#2 best model for Image Super-Resolution on BSD100 - 4x upscaling
In this paper, we show that it is possible to recover textures faithful to semantic classes.
#30 best model for Image Super-Resolution on Set5 - 4x upscaling
We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model.
#5 best model for Conditional Image Generation on ImageNet 128x128