Super resolution is the task of taking an input of a low resolution (LR) and upscaling it to that of a high resolution.
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
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.
#10 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.
We propose an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN).
#18 best model for Image Super-Resolution on Set5 - 4x 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 Set5 - 4x upscaling
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.
#6 best model for Image Super-Resolution on Set5 - 4x upscaling
In our work, we instead propose an adversarial training for video super-resolution that leads to temporally coherent solutions without sacrificing spatial detail.
Within Music Information Retrieval (MIR), prominent tasks -- including pitch-tracking, source-separation, super-resolution, and synthesis -- typically call for specialised methods, despite their similarities.