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 1721–1730 of 3874 papers

TitleStatusHype
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational AutoencoderCode0
Self-Adaptive Reality-Guided Diffusion for Artifact-Free Super-ResolutionCode0
Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution MicroscopyCode0
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution—0
A Study in Dataset Pruning for Image Super-Resolution—0
LaMAR: Laplacian Pyramid for Multimodal Adaptive Super Resolution (Student Abstract)—0
Time-series Initialization and Conditioning for Video-agnostic Stabilization of Video Super-Resolution using Recurrent Networks—0
Deep Generative Model based Rate-Distortion for Image Downscaling AssessmentCode0
Hyperspectral Neural Radiance Fields—0
QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified