546 papers with code • 0 benchmarks • 20 datasets
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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( Credit: MemNet )
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.
Ranked #3 on Image Super-Resolution on WebFace - 8x upscaling
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
This means that the super-resolution (SR) operation is performed in HR space.
Ranked #1 on Video Super-Resolution on Xiph HD - 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).
Ranked #2 on Face Hallucination on FFHQ 512 x 512 - 16x upscaling
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.
Ranked #9 on Image Super-Resolution on FFHQ 256 x 256 - 4x upscaling (SSIM metric)
We propose a method that, given a single image with its estimated background, outputs the object's appearance and position in a series of sub-frames as if captured by a high-speed camera (i. e. temporal super-resolution).
Ranked #1 on Video Super-Resolution on Falling Objects
Additionally, we propose a first set of metrics to quantitatively evaluate the accuracy as well as the perceptual quality of the temporal evolution.
Ranked #10 on Video Super-Resolution on Vid4 - 4x upscaling (PSNR metric)
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.
Ranked #1 on Color Image Denoising on CBSD68 sigma50