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 3351–3360 of 3874 papers

TitleStatusHype
Multigrid Backprojection Super-Resolution and Deep Filter VisualizationCode0
OAIR: Object-Aware Image Retargeting Using PSO and Aesthetic Quality AssessmentCode0
Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New MethodCode0
A Matrix-in-matrix Neural Network for Image Super ResolutionCode0
Multiframe Motion Coupling for Video Super ResolutionCode0
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational AutoencoderCode0
Multi-Feature Aggregation in Diffusion Models for Enhanced Face Super-ResolutionCode0
MSFNet-CPD: Multi-Scale Cross-Modal Fusion Network for Crop Pest DetectionCode0
Volumetric Isosurface Rendering with Deep Learning-Based Super-ResolutionCode0
Enhancing Amyloid PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion ModelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified