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 826850 of 1589 papers

TitleStatusHype
HiREN: Towards Higher Supervision Quality for Better Scene Text Image Super-Resolution0
Overcoming Distribution Mismatch in Quantizing Image Super-Resolution NetworksCode0
ICF-SRSR: Invertible scale-Conditional Function for Self-Supervised Real-world Single Image Super-Resolution0
NLCUnet: Single-Image Super-Resolution Network with Hairline Details0
PartDiff: Image Super-resolution with Partial Diffusion Models0
Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network0
A comparative analysis of SRGAN models0
Soft-IntroVAE for Continuous Latent space Image Super-Resolution0
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
Spatio-Temporal Perception-Distortion Trade-off in Learned Video SRCode0
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution0
SPDER: Semiperiodic Damping-Enabled Object Representation0
Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging SystemsCode0
Semantic Segmentation Using Super Resolution Technique as Pre-Processing0
Real-World Video for Zoom Enhancement based on Spatio-Temporal Coupling0
HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models0
Optical Coherence Tomography Image Enhancement via Block Hankelization and Low Rank Tensor Network Approximation0
Evaluating Loss Functions and Learning Data Pre-Processing for Climate Downscaling Deep Learning Models0
Learning Image-Adaptive Codebooks for Class-Agnostic Image Restoration0
A Unified Framework to Super-Resolve Face Images of Varied Low Resolutions0
Scale Guided Hypernetwork for Blind Super-Resolution Image Quality AssessmentCode0
EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection0
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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