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 576–600 of 3874 papers

TitleStatusHype
Use of triplet loss for facial restoration in low-resolution images—0
aTENNuate: Optimized Real-time Speech Enhancement with Deep SSMs on Raw Audio—0
Perceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization ProblemCode0
LMLT: Low-to-high Multi-Level Vision Transformer for Image Super-ResolutionCode1
Solving Video Inverse Problems Using Image Diffusion Models—0
SeCo-INR: Semantically Conditioned Implicit Neural Representations for Improved Medical Image Super-Resolution—0
EarthGen: Generating the World from Top-Down ViewsCode0
DMRA: An Adaptive Line Spectrum Estimation Method through Dynamical Multi-Resolution of Atoms—0
Attention-Guided Multi-scale Interaction Network for Face Super-Resolution—0
Rethinking Image Super-Resolution from Training Data PerspectivesCode1
HiTSR: A Hierarchical Transformer for Reference-based Super-ResolutionCode0
GameIR: A Large-Scale Synthesized Ground-Truth Dataset for Image Restoration over Gaming Content—0
Beyond MR Image Harmonization: Resolution Matters Too—0
Enhanced Control for Diffusion Bridge in Image RestorationCode0
Super-Resolution works for coastal simulations—0
ChartEye: A Deep Learning Framework for Chart Information Extraction—0
Histo-Diffusion: A Diffusion Super-Resolution Method for Digital Pathology with Comprehensive Quality Assessment—0
Multi-Feature Aggregation in Diffusion Models for Enhanced Face Super-ResolutionCode0
A Preliminary Exploration Towards General Image Restoration—0
Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven ApproachCode1
Cascaded Temporal Updating Network for Efficient Video Super-ResolutionCode1
Particle-Filtering-based Latent Diffusion for Inverse Problems—0
FreqINR: Frequency Consistency for Implicit Neural Representation with Adaptive DCT Frequency Loss—0
ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral ImagesCode0
SIMPLE: Simultaneous Multi-Plane Self-Supervised Learning for Isotropic MRI Restoration from Anisotropic Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified