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 3461–3470 of 3874 papers

TitleStatusHype
Efficient Integer-Arithmetic-Only Convolutional Neural NetworksCode0
Lightweight Feature Fusion Network for Single Image Super-ResolutionCode0
Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid NetworkCode0
Perceptual cGAN for MRI Super-resolutionCode0
Center Smoothing: Certified Robustness for Networks with Structured OutputsCode0
Perceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization ProblemCode0
Lightweight and Robust Representation of Economic Scales from Satellite ImageryCode0
Efficient Event Stream Super-Resolution with Recursive Multi-Branch FusionCode0
Lightweight and Efficient Image Super-Resolution with Block State-based Recursive NetworkCode0
Perceptual deep depth super-resolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified