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 476500 of 1589 papers

TitleStatusHype
NLCUnet: Single-Image Super-Resolution Network with Hairline Details0
On the Effectiveness of Spectral Discriminators for Perceptual Quality ImprovementCode1
PartDiff: Image Super-resolution with Partial Diffusion Models0
Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network0
Towards Robust Scene Text Image Super-resolution via Explicit Location EnhancementCode1
Soft-IntroVAE for Continuous Latent space Image Super-Resolution0
A comparative analysis of SRGAN models0
DARTS: Double Attention Reference-based Transformer for Super-resolutionCode1
Reconstructed Convolution Module Based Look-Up Tables for Efficient Image Super-ResolutionCode1
MaxSR: Image Super-Resolution Using Improved MaxViT0
DWA: Differential Wavelet Amplifier for Image Super-ResolutionCode0
Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network for Remote Sensing Image Super-Resolution0
Compound Attention and Neighbor Matching Network for Multi-contrast MRI Super-resolution0
DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution ModelsCode1
Spatio-Temporal Perception-Distortion Trade-off in Learned Video SRCode0
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution0
WaveMixSR: A Resource-efficient Neural Network for Image Super-resolutionCode1
SPDER: Semiperiodic Damping-Enabled Object Representation0
Semantic Segmentation Using Super Resolution Technique as Pre-Processing0
Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging SystemsCode0
Real-World Video for Zoom Enhancement based on Spatio-Temporal Coupling0
HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models0
Evaluating Loss Functions and Learning Data Pre-Processing for Climate Downscaling Deep Learning Models0
Optical Coherence Tomography Image Enhancement via Block Hankelization and Low Rank Tensor Network Approximation0
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluationCode1
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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