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 2661–2670 of 3874 papers

TitleStatusHype
Pixel Co-Occurence Based Loss Metrics for Super Resolution Texture Recovery—0
Boosting Diffusion-Based Text Image Super-Resolution Model Towards Generalized Real-World Scenarios—0
Boomerang: Local sampling on image manifolds using diffusion models—0
Super-resolution of Time-series Labels for Bootstrapped Event Detection—0
γ-Net: Superresolving SAR Tomographic Inversion via Deep Learning—0
A Unified Model for Compressed Sensing MRI Across Undersampling Patterns—0
BOLD: Boolean Logic Deep Learning—0
Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modeling—0
Supervised Image Translation from Visible to Infrared Domain for Object Detection—0
Block-Based Multi-Scale Image Rescaling—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified