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

Showing 23262350 of 3874 papers

TitleStatusHype
Deep Fourier Up-SamplingCode0
Cost-effective photonic super-resolution millimeter-wave joint radar-communication system using self-coherent detection0
Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images0
Learning Texture Transformer Network for Light Field Super-Resolution0
Flexible Alignment Super-Resolution Network for Multi-Contrast MRICode0
A Simple Plugin for Transforming Images to Arbitrary Scales0
Nanoscopic distribution of VAChT and VGLUT3 in striatal cholinergic varicosities suggests colocalization and segregation of the two transporters in synaptic vesicles0
MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-ResolutionCode0
Imagen Video: High Definition Video Generation with Diffusion Models0
Low-Resolution Action Recognition for Tiny Actions Challenge0
Deep Sparse and Low-Rank Prior for Hyperspectral Image DenoisingCode0
Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances0
Scaling Laws For Deep Learning Based Image ReconstructionCode0
DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image0
Effective Invertible Arbitrary Image Rescaling0
Deep generative model super-resolves spatially correlated multiregional climate data0
Analytic Optimization-Based Microbubble Tracking in Ultrasound Super-Resolution Microscopy0
3D Super-Resolution Imaging Method for Distributed Millimeter-wave Automotive Radar System0
Gemino: Practical and Robust Neural Compression for Video Conferencing0
Multi-Field De-interlacing using Deformable Convolution Residual Blocks and Self-Attention0
Recurrent Super-Resolution Method for Enhancing Low Quality Thermal Facial Data0
Diabetic foot ulcers monitoring by employing super resolution and noise reduction deep learning techniques0
Synthesis of realistic fetal MRI with conditional Generative Adversarial Networks0
Perception-Distortion Trade-off in the SR Space Spanned by Flow Models0
MMSR: Multiple-Model Learned Image Super-Resolution Benefiting From Class-Specific Image Priors0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41Unverified