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 3661–3670 of 3874 papers

TitleStatusHype
Detail-revealing Deep Video Super-resolutionCode0
Depth Super-Resolution Meets Uncalibrated Photometric StereoCode0
Image Reconstruction with Predictive Filter FlowCode0
Beyond Subspace Isolation: Many-to-Many Transformer for Light Field Image Super-resolutionCode0
Algorithmic Guarantees for Inverse Imaging with Untrained Network PriorsCode0
Dense xUnit NetworksCode0
Denoising Prior Driven Deep Neural Network for Image RestorationCode0
Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold SimplificationCode0
Denoising Graph Super-Resolution towards Improved Collider Event ReconstructionCode0
Recovering high-quality FODs from a reduced number of diffusion-weighted images using a model-driven deep learning architectureCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified