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 2526–2550 of 3874 papers

TitleStatusHype
One-Shot Model for Mixed-Precision Quantization—0
Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution—0
Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution—0
Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment—0
CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images—0
One Target, Many Views: Multi-User Fusion for Collaborative Uplink ISAC—0
Cephalogram Synthesis and Landmark Detection in Dental Cone-Beam CT Systems—0
Online 4D Ultrasound-Guided Robotic Tracking Enables 3D Ultrasound Localisation Microscopy with Large Tissue Displacements—0
Online Streaming Video Super-Resolution with Convolutional Look-Up Table—0
Online Video Super-Resolution with Convolutional Kernel Bypass Graft—0
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution—0
CasSR: Activating Image Power for Real-World Image Super-Resolution—0
Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance—0
Cas-DiffCom: Cascaded diffusion model for infant longitudinal super-resolution 3D medical image completion—0
Cascaded Diffusion Models for High Fidelity Image Generation—0
On the modern deep learning approaches for precipitation downscaling—0
On the Robustness of Normalizing Flows for Inverse Problems in Imaging—0
On The Role of Alias and Band-Shift for Sentinel-2 Super-Resolution—0
On the Use of Singular Value Decomposition as a Clutter Filter for Ultrasound Flow Imaging—0
On training deep networks for satellite image super-resolution—0
On Versatile Video Coding at UHD with Machine-Learning-Based Super-Resolution—0
OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution—0
Cascaded Detail-Preserving Networks for Super-Resolution of Document Images—0
Uncertainty-Driven Loss for Single Image Super-Resolution—0
OPE-SR: Orthogonal Position Encoding for Designing a Parameter-free Upsampling Module in Arbitrary-scale Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified