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 676700 of 1589 papers

TitleStatusHype
Single Image Super-Resolution Based on Capsule Neural NetworksCode1
MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-ResolutionCode0
Accurate Image Restoration with Attention Retractable TransformerCode1
From Face to Natural Image: Learning Real Degradation for Blind Image Super-ResolutionCode1
Multi-scale Attention Network for Single Image Super-ResolutionCode1
Effective Invertible Arbitrary Image Rescaling0
A heterogeneous group CNN for image super-resolutionCode1
Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark DatasetCode1
Face Super-Resolution Using Stochastic Differential EquationsCode1
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and RestorationCode2
Recurrent Super-Resolution Method for Enhancing Low Quality Thermal Facial Data0
KXNet: A Model-Driven Deep Neural Network for Blind Super-ResolutionCode1
MMSR: Multiple-Model Learned Image Super-Resolution Benefiting From Class-Specific Image Priors0
Lightweight Spatial-Channel Adaptive Coordination of Multilevel Refinement Enhancement Network for Image Reconstruction0
Generative Adversarial Super-Resolution at the Edge with Knowledge DistillationCode1
Diffusion Models: A Comprehensive Survey of Methods and ApplicationsCode4
XCAT -- Lightweight Quantized Single Image Super-Resolution using Heterogeneous Group Convolutions and Cross Concatenation0
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noiseCode0
Joint Learning Content and Degradation Aware Feature for Blind Super-ResolutionCode1
DSR: Towards Drone Image Super-ResolutionCode1
SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-ResolutionCode1
AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic ModelsCode1
RZSR: Reference-based Zero-Shot Super-Resolution with Depth Guided Self-ExemplarsCode1
Sliding Window Recurrent Network for Efficient Video Super-ResolutionCode1
Fast Nearest Convolution for Real-Time Efficient Image Super-ResolutionCode1
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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