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 401425 of 1589 papers

TitleStatusHype
Diffusion-based Blind Text Image Super-ResolutionCode1
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse ProblemsCode1
Learned Image Downscaling for Upscaling using Content Adaptive ResamplerCode1
Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-ResolutionCode1
Efficient and Degradation-Adaptive Network for Real-World Image Super-ResolutionCode1
Efficient Image Super-Resolution Using Pixel AttentionCode1
DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-ResolutionCode1
HSRMamba: Contextual Spatial-Spectral State Space Model for Single Image Hyperspectral Super-ResolutionCode1
HST: Hierarchical Swin Transformer for Compressed Image Super-resolutionCode1
Human Guided Ground-truth Generation for Realistic Image Super-resolutionCode1
AdaDM: Enabling Normalization for Image Super-ResolutionCode1
Discovering Distinctive "Semantics" in Super-Resolution NetworksCode1
Creating High Resolution Images with a Latent Adversarial GeneratorCode1
edge-SR: Super-Resolution For The MassesCode1
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and BeyondCode1
Real-World Light Field Image Super-Resolution via Degradation ModulationCode1
C3-STISR: Scene Text Image Super-resolution with Triple CluesCode1
Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous DatasetsCode1
Learning Correction Filter via Degradation-Adaptive Regression for Blind Single Image Super-ResolutionCode1
Does Diffusion Beat GAN in Image Super Resolution?Code1
CADyQ: Content-Aware Dynamic Quantization for Image Super-ResolutionCode1
Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual ReconstructionCode1
Attaining Real-Time Super-Resolution for Microscopic Images Using GANCode1
Joint Learning Content and Degradation Aware Feature for Blind Super-ResolutionCode1
A Tree-guided CNN for 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