SOTAVerified

Image Super-Resolution

Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.

Papers

Showing 701725 of 1589 papers

TitleStatusHype
Hybrid Function Sparse Representation towards Image Super ResolutionCode0
Data-driven Super-Resolution of Flood Inundation Maps using Synthetic SimulationsCode0
Lightweight and Efficient Image Super-Resolution with Block State-based Recursive NetworkCode0
Learning Series-Parallel Lookup Tables for Efficient Image Super-ResolutionCode0
Learning Parallax Attention for Stereo Image Super-ResolutionCode0
Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super ResolutionCode0
BadRefSR: Backdoor Attacks Against Reference-based Image Super ResolutionCode0
Lightweight Feature Fusion Network for Single Image Super-ResolutionCode0
CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature DenoiserCode0
Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-ResolutionCode0
DACN: Dual-Attention Convolutional Network for Hyperspectral Image Super-ResolutionCode0
Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsCode0
Feedback Network for Image Super-ResolutionCode0
Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution ForestsCode0
Learning Accurate and Enriched Features for Stereo Image Super-ResolutionCode0
LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-ResolutionCode0
LAR-SR: A Local Autoregressive Model for Image Super-ResolutionCode0
LatticeNet: Towards Lightweight Image Super-resolution with Lattice BlockCode0
Learning a No-Reference Quality Metric for Single-Image Super-ResolutionCode0
Lightweight Image Super-Resolution with Adaptive Weighted Learning NetworkCode0
Kernel Modeling Super-Resolution on Real Low-Resolution ImagesCode0
Image-Adaptive GAN based ReconstructionCode0
FC^2N: Fully Channel-Concatenated Network for Single Image Super-ResolutionCode0
Joint Maximum Purity Forest with Application to Image Super-ResolutionCode0
Fast Omni-Directional Image Super-Resolution: Adapting the Implicit Image Function with Pixel and Semantic-Wise Spherical Geometric PriorsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR29.54Unverified
2HMA†PSNR29.51Unverified
3Hi-IR-LPSNR29.49Unverified
4HAT-LPSNR29.47Unverified
5HAT_FIRPSNR29.44Unverified
6DRCTPSNR29.4Unverified
7HATPSNR29.38Unverified
8CPAT+PSNR29.36Unverified
9SwinFIRPSNR29.36Unverified
10CPATPSNR29.34Unverified
#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR28.16Unverified
2HMA†PSNR28.13Unverified
3Hi-IR-LPSNR28.13Unverified
4HAT-LPSNR28.09Unverified
5HAT_FIRPSNR28.07Unverified
6CPAT+PSNR28.06Unverified
7DRCTPSNR28.06Unverified
8HATPSNR28.05Unverified
9CPATPSNR28.04Unverified
10SwinFIRPSNR28.03Unverified
#ModelMetricClaimedVerifiedStatus
1Hi-IR-LPSNR28.72Unverified
2DRCT-LPSNR28.7Unverified
3HMA†PSNR28.69Unverified
4HAT-LPSNR28.6Unverified
5HAT_FIRPSNR28.43Unverified
6DRCTPSNR28.4Unverified
7HATPSNR28.37Unverified
8CPAT+PSNR28.33Unverified
9CPATPSNR28.22Unverified
10PFTPSNR28.2Unverified