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 376400 of 1589 papers

TitleStatusHype
edge-SR: Super-Resolution For The MassesCode1
Light Field Image Super-Resolution with TransformersCode1
spectrai: A deep learning framework for spectral dataCode1
Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-ResolutionCode1
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image RescalingCode1
Discovering Distinctive "Semantics" in Super-Resolution NetworksCode1
Fourier Series Expansion Based Filter Parametrization for Equivariant ConvolutionsCode1
Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual ReconstructionCode1
Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and KernelCode1
Text Prior Guided Scene Text Image Super-resolutionCode1
Fairness for Image Generation with Uncertain Sensitive AttributesCode1
One-to-many Approach for Improving Super-ResolutionCode1
Image Super-Resolution With Non-Local Sparse AttentionCode1
Practical Single-Image Super-Resolution Using Look-Up TableCode1
Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV MinimizationCode1
Task Transformer Network for Joint MRI Reconstruction and Super-ResolutionCode1
Variational AutoEncoder for Reference based Image Super-ResolutionCode1
MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-ResolutionCode1
Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity EstimationCode1
CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed TomographyCode1
Permutation invariance and uncertainty in multitemporal image super-resolutionCode1
Towards Compact Single Image Super-Resolution via Contrastive Self-distillationCode1
MIASSR: An Approach for Medical Image Arbitrary Scale Super-ResolutionCode1
Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile DevicesCode1
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and BeyondCode1
Show:102550
← PrevPage 16 of 64Next →

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