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 681–690 of 3874 papers

TitleStatusHype
M^3:Manipulation Mask Manufacturer for Arbitrary-Scale Super-Resolution Mask—0
Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning—0
Data Overfitting for On-Device Super-Resolution with Dynamic Algorithm and Compiler Co-DesignCode0
Adversarial Magnification to Deceive Deepfake Detection through Super ResolutionCode1
Real HSI-MSI-PAN image dataset for the hyperspectral/multi-spectral/panchromatic image fusion and super-resolution fieldsCode1
Efficient Terrain Stochastic Differential Efficient Terrain Stochastic Differential Equations for Multipurpose Digital Elevation Model Restoration—0
DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration ModelsCode2
DaBiT: Depth and Blur informed Transformer for Joint Refocusing and Super-ResolutionCode0
Preserving Full Degradation Details for Blind Image Super-ResolutionCode1
CSAKD: Knowledge Distillation with Cross Self-Attention for Hyperspectral and Multispectral Image FusionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified