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 901925 of 1589 papers

TitleStatusHype
Multi-Attributed and Structured Text-to-Face Synthesis0
edge-SR: Super-Resolution For The MassesCode1
SwinIR: Image Restoration Using Swin TransformerCode3
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search0
spectrai: A deep learning framework for spectral dataCode1
Light Field Image Super-Resolution with TransformersCode1
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image RescalingCode1
Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-ResolutionCode1
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration0
FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution0
Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution0
Discovering Distinctive "Semantics" in Super-Resolution NetworksCode1
Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive FieldsCode0
Fourier Series Expansion Based Filter Parametrization for Equivariant ConvolutionsCode1
MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN0
LAConv: Local Adaptive Convolution for Image Fusion0
RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank0
Multi-Attention Generative Adversarial Network for Remote Sensing Image Super-Resolution0
Effectiveness of State-of-the-Art Super Resolution Algorithms in Surveillance Environment0
Image restoration quality assessment based on regional differential information entropy0
A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution0
Blind Image Super-Resolution: A Survey and Beyond0
Impact of deep learning-based image super-resolution on binary signal detection0
Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual ReconstructionCode1
Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and KernelCode1
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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