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 451475 of 1589 papers

TitleStatusHype
EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale DatasetCode1
Edge-enhanced Feature Distillation Network for Efficient Super-ResolutionCode1
Rethinking Image Super-Resolution from Training Data PerspectivesCode1
Revisiting RCAN: Improved Training for Image Super-ResolutionCode1
From Face to Natural Image: Learning Real Degradation for Blind Image Super-ResolutionCode1
EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-ResolutionCode1
EDPN: Enhanced Deep Pyramid Network for Blurry Image RestorationCode1
Robust Single-Image Super-Resolution via CNNs and TV-TV MinimizationCode1
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image SynthesisCode1
A Tree-guided CNN for image super-resolutionCode1
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-ResolutionCode1
Scale-Aware Dynamic Network for Continuous-Scale Super-ResolutionCode1
Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI ScansCode1
A Systematic Survey of Deep Learning-based Single-Image Super-ResolutionCode1
Fully 11 Convolutional Network for Lightweight Image Super-ResolutionCode1
Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-ResolutionCode1
Frequency Domain-based Perceptual Loss for Super ResolutionCode1
Efficient CNN-based Super Resolution Algorithms for mmWave Mobile Radar ImagingCode1
Efficient Conditional Diffusion Model with Probability Flow Sampling for Image Super-resolutionCode1
Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and ReportCode1
Flow-based Kernel Prior with Application to Blind Super-ResolutionCode1
Flexible Style Image Super-Resolution using Conditional ObjectiveCode1
Efficient Image Super-Resolution Using Pixel AttentionCode1
Efficient Image Super-Resolution using Vast-Receptive-Field AttentionCode1
Fourier Series Expansion Based Filter Parametrization for Equivariant ConvolutionsCode1
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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