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 14011425 of 1589 papers

TitleStatusHype
SinSR: Diffusion-Based Image Super-Resolution in a Single StepCode0
Physics-Informed Ensemble Representation for Light-Field Image Super-ResolutionCode0
EnhanceNet: Single Image Super-Resolution Through Automated Texture SynthesisCode0
Lightweight and Efficient Image Super-Resolution with Block State-based Recursive NetworkCode0
Efficient Single Image Super Resolution using Enhanced Learned Group ConvolutionsCode0
Unsupervised and Unregistered Hyperspectral Image Super-Resolution with Mutual Dirichlet-NetCode0
Learning Series-Parallel Lookup Tables for Efficient Image Super-ResolutionCode0
Learning Parallax Attention for Stereo Image Super-ResolutionCode0
A deep learning framework for morphologic detail beyond the diffraction limit in infrared spectroscopic imagingCode0
Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super ResolutionCode0
Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution ForestsCode0
DDR: Exploiting Deep Degradation Response as Flexible Image DescriptorCode0
Efficient Residual Dense Block Search for Image Super-ResolutionCode0
Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsCode0
Learning a No-Reference Quality Metric for Single-Image Super-ResolutionCode0
Practical Manipulation Model for Robust Deepfake DetectionCode0
Learning Accurate and Enriched Features for Stereo Image Super-ResolutionCode0
A Deep Journey into Super-resolution: A surveyCode0
LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-ResolutionCode0
LatticeNet: Towards Lightweight Image Super-resolution with Lattice BlockCode0
LAR-SR: A Local Autoregressive Model for Image Super-ResolutionCode0
Efficient Integer-Arithmetic-Only Convolutional Neural NetworksCode0
Proba-V-ref: Repurposing the Proba-V challenge for reference-aware super resolutionCode0
Text-Aware Real-World Image Super-Resolution via Diffusion Model with Joint Segmentation DecodersCode0
LAP: a Linearize and Project Method for Solving Inverse Problems with Coupled VariablesCode0
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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