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 2081–2090 of 3874 papers

TitleStatusHype
Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool—0
Empirical Bayesian image restoration by Langevin sampling with a denoising diffusion implicit prior—0
Emu Edit: Precise Image Editing via Recognition and Generation Tasks—0
End-to-End Adaptive Monte Carlo Denoising and Super-Resolution—0
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks—0
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks—0
End-to-End Learning of Video Super-Resolution with Motion Compensation—0
End-to-end pipeline for simultaneous temperature estimation and super resolution of low-cost uncooled infrared camera frames for precision agriculture applications—0
End-To-End Trainable Video Super-Resolution Based on a New Mechanism for Implicit Motion Estimation and Compensation—0
Energy-Inspired Self-Supervised Pretraining for Vision Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified