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 1591–1600 of 3874 papers

TitleStatusHype
Hybrid Inexact BCD for Coupled Structured Matrix Factorization in Hyperspectral Super-ResolutionCode0
Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-ResolutionCode0
A Dictionary Based Approach for Removing Out-of-Focus BlurCode0
Hybrid Function Sparse Representation towards Image Super ResolutionCode0
Hyperspectral and multispectral image fusion with arbitrary resolution through self-supervised representationsCode0
HR-INR: Continuous Space-Time Video Super-Resolution via Event CameraCode0
3DAttGAN: A 3D Attention-based Generative Adversarial Network for Joint Space-Time Video Super-ResolutionCode0
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational AutoencoderCode0
Domain Transfer in Latent Space (DTLS) Wins on Image Super-Resolution -- a Non-Denoising ModelCode0
A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGACode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified