SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1451–1475 of 3874 papers

TitleStatusHype
Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution—0
HyperINR: A Fast and Predictive Hypernetwork for Implicit Neural Representations via Knowledge Distillation—0
Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement—0
Towards Realistic Ultrasound Fetal Brain Imaging SynthesisCode1
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-ResolutionCode1
Super-Resolving Face Image by Facial Parsing InformationCode0
Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-ResolutionCode1
Acceleration-Based Kalman Tracking for Super-Resolution Ultrasound Imaging in vivo—0
CoReFusion: Contrastive Regularized Fusion for Guided Thermal Super-ResolutionCode1
Generative Diffusion Prior for Unified Image Restoration and Enhancement—0
Real-time 6K Image Rescaling with Rate-distortion OptimizationCode1
AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models—0
CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images—0
Burstormer: Burst Image Restoration and Enhancement TransformerCode1
Tunable Convolutions with Parametric Multi-Loss OptimizationCode1
Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation TransformerCode1
Image-to-image domain adaptation for vehicle re-identification—0
Dual Circle Contrastive Learning-Based Blind Image Super-Resolution—0
Random Weights Networks Work as Loss Prior Constraint for Image Restoration—0
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration—0
Operational Neural Networks for Parameter-Efficient Hyperspectral Single-Image Super-ResolutionCode0
Cascaded Local Implicit Transformer for Arbitrary-Scale Super-ResolutionCode1
Implicit Diffusion Models for Continuous Super-ResolutionCode2
CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super ResolutionCode1
Single-subject Multi-contrast MRI Super-resolution via Implicit Neural RepresentationsCode1
Show:102550
← PrevPage 59 of 155Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified