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 1711–1720 of 3874 papers

TitleStatusHype
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution—0
Distortion-aware super-resolution for planetary exploration imagesCode0
RefQSR: Reference-based Quantization for Image Super-Resolution Networks—0
Super-Resolution Analysis for Landfill Waste Classification—0
Video Interpolation with Diffusion Models—0
SGDFormer: One-stage Transformer-based Architecture for Cross-Spectral Stereo Image Guided Denoising—0
Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D—0
Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network—0
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational AutoencoderCode0
Climate Downscaling: A Deep-Learning Based Super-resolution Model of Precipitation Data with Attention Block and Skip Connections—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified