487 papers with code • 0 benchmarks • 19 datasets

Super resolution is the task of taking an input of a low resolution (LR) and upscaling it to that of a high resolution.

( Credit: MemNet )

Latest papers with code

SinIR: Efficient General Image Manipulation with Single Image Reconstruction

YooJiHyeong/SinIR 14 Jun 2021

We train our model on a single image with cascaded multi-scale learning, where each network at each scale is responsible for image reconstruction.

Denoising Image Manipulation +3

14 Jun 2021

Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV Minimization

marijavella/hs-sr-tvtv 13 Jun 2021

Such methods, however, cannot guarantee that the input measurements are satisfied in the recovered image, since the learned parameters by the network are applied to every test image.

Hyperspectral Image Super-Resolution Image Super-Resolution

13 Jun 2021

Video Super-Resolution Transformer

caojiezhang/VSR-Transformer 12 Jun 2021

Specifically, to tackle the first issue, we present a spatial-temporal convolutional self-attention layer with a theoretical understanding to exploit the locality information.

Optical Flow Estimation Video Super-Resolution

12 Jun 2021

Task Transformer Network for Joint MRI Reconstruction and Super-Resolution

chunmeifeng/T2Net 12 Jun 2021

Then, a task transformer module is designed to embed and synthesize the relevance between the two tasks.

MRI Reconstruction Super-Resolution

12 Jun 2021

Variational AutoEncoder for Reference based Image Super-Resolution

Holmes-Alan/RefVAE 8 Jun 2021

In this paper, we propose a novel reference based image super-resolution approach via Variational AutoEncoder (RefVAE).

Image Super-Resolution SSIM

08 Jun 2021

Noise Conditional Flow Model for Learning the Super-Resolution Space

younggeun-kim/NCSR 6 Jun 2021

Although SRFlow tried to account for ill-posed nature of the super-resolution by predicting multiple high-resolution images given a low-resolution image, there is room to improve the diversity and visual quality.


06 Jun 2021

Robust Reference-based Super-Resolution via C2-Matching

yumingj/C2-Matching 3 Jun 2021

However, performing local transfer is difficult because of two gaps between input and reference images: the transformation gap (e. g. scale and rotation) and the resolution gap (e. g. HR and LR).


03 Jun 2021

SNIPS: Solving Noisy Inverse Problems Stochastically

bahjat-kawar/snips_torch 31 May 2021

In this work we introduce a novel stochastic algorithm dubbed SNIPS, which draws samples from the posterior distribution of any linear inverse problem, where the observation is assumed to be contaminated by additive white Gaussian noise.

Compressive Sensing Deblurring +1

31 May 2021