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

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Showing 2651–2675 of 3874 papers

TitleStatusHype
Stereo Image Rain Removal via Dual-View Mutual Attention—0
StereoINR: Cross-View Geometry Consistent Stereo Super Resolution with Implicit Neural Representation—0
Stochastic Attribute Modeling for Face Super-Resolution—0
Stochastic Deep Restoration Priors for Imaging Inverse Problems—0
Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser—0
Stochastic Super-Resolution For Gaussian Textures—0
Stochastic super-resolution for Gaussian microtextures—0
Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models—0
Stop-and-go wave super-resolution reconstruction via iterative refinement—0
Stroke-based Cyclic Amplifier: Image Super-Resolution at Arbitrary Ultra-Large Scales—0
Structural Similarity in Deep Features: Image Quality Assessment Robust to Geometrically Disparate Reference—0
Structured Bayesian Gaussian process latent variable model—0
Structured illumination microscopy for dual-modality 3D sub-diffraction resolution fluorescence and refractive-index reconstruction—0
Structure Flow-Guided Network for Real Depth Super-Resolution—0
Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning—0
Structure Tensor Based Image Interpolation Method—0
Studying Very Low Resolution Recognition Using Deep Networks—0
Study of Subjective and Objective Quality in Super-Resolution Enhanced Broadcast Images on a Novel SR-IQA Dataset—0
Exploiting Style and Attention in Real-World Super-Resolution—0
StyleDEM: a Versatile Model for Authoring Terrains—0
StyleGAN-induced data-driven regularization for inverse problems—0
Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging—0
Sub-Pixel Registration of Wavelet-Encoded Images—0
Subspace-Based Super-Resolution Sensing for Bi-Static ISAC with Clock Asynchronism—0
Subsurface Depths Structure Maps Reconstruction with Generative Adversarial Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified