Image super-resolution (SR) techniques reconstruct a higher-resolution image or sequence from the observed lower-resolution images. Usually the benchmarks are single-image super-resolution (SISR) tasks.
( Image credit: BasicSR )
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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.
#3 best model for Image Super-Resolution on VggFace2 - 8x 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.
We propose an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN).
#10 best model for Image Super-Resolution on Urban100 - 2x upscaling
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).
#2 best model for Image Super-Resolution on PIRM-test
Additionally, we propose a first set of metrics to quantitatively evaluate the accuracy as well as the perceptual quality of the temporal evolution.
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.
#9 best model for Image Super-Resolution on Manga109 - 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.
#8 best model for Video Super-Resolution on Vid4 - 4x upscaling
Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN).
#2 best model for Image Super-Resolution on FFHQ 1024 x 1024 - 4x upscaling