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 9761000 of 1589 papers

TitleStatusHype
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics0
Bilateral Network with Channel Splitting Network and Transformer for Thermal Image Super-Resolution0
Multi-Modality Image Super-Resolution using Generative Adversarial NetworksCode0
Real-World Single Image Super-Resolution Under Rainy Condition0
A No-Reference Deep Learning Quality Assessment Method for Super-resolution Images Based on Frequency Maps0
Robust Deep Ensemble Method for Real-world Image DenoisingCode0
Patch-based image Super Resolution using generalized Gaussian mixture model0
Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution0
Real-Time Super-Resolution for Real-World Images on Mobile Devices0
Super-resolving 2D stress tensor field conserving equilibrium constraints using physics informed U-Net0
Efficient Multi-Purpose Cross-Attention Based Image Alignment Block for Edge Devices0
Image Reconstruction of Multi Branch Feature Multiplexing Fusion Network with Mixed Multi-layer Attention0
Textural-Perceptual Joint Learning for No-Reference Super-Resolution Image Quality AssessmentCode0
Cross-domain heterogeneous residual network for single image super-resolutionCode0
Diverse super-resolution with pretrained deep hiererarchical VAEs0
A Comparative Study of Feature Expansion Unit for 3D Point Cloud Upsampling0
Semantically Accurate Super-Resolution Generative Adversarial Networks0
SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution0
Generative Adversarial Networks for Image Super-Resolution: A Survey0
IMDeception: Grouped Information Distilling Super-Resolution Network0
NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results0
GHM Wavelet Transform for Deep Image Super Resolution0
Super-resolved multi-temporal segmentation with deep permutation-invariant networks0
Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution0
Single Image Internal Distribution Measurement Using Non-Local Variational Autoencoder0
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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
8SwinFIRPSNR29.36Unverified
9CPAT+PSNR29.36Unverified
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