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 1751–1760 of 3874 papers

TitleStatusHype
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Non-Premixed Combustion on Non-Uniform Meshes and Demonstration of an Accelerated Simulation Workflow—0
Conditioning and Sampling in Variational Diffusion Models for Speech Super-ResolutionCode0
Generalized Matrix-Pencil Approach to Estimation of Complex Exponentials with Gapped Data—0
Three more Decades in Array Signal Processing Research: An Optimization and Structure Exploitation Perspective—0
Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency GuidanceCode0
A Regularized Conditional GAN for Posterior Sampling in Image Recovery ProblemsCode0
High-Resolution Image Editing via Multi-Stage Blended DiffusionCode1
Iris super-resolution using CNNs: is photo-realism important to iris recognition?—0
Single Image Super-Resolution via a Dual Interactive Implicit Neural NetworkCode1
How Real is Real: Evaluating the Robustness of Real-World Super Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified