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 2361–2370 of 3874 papers

TitleStatusHype
Multi-Field De-interlacing using Deformable Convolution Residual Blocks and Self-Attention—0
Multi-frame image super-resolution with fast upscaling technique—0
Triple Attention Mixed Link Network for Single Image Super Resolution—0
Multi-Frame Super-Resolution Combining Demons Registration and Regularized Bayesian Reconstruction—0
Multi-frame Super-resolution from Noisy Data—0
Multi-Frame Super-Resolution Reconstruction with Applications to Medical Imaging—0
Multi-Frequency Representation Enhancement with Privilege Information for Video Super-Resolution—0
Multi-grained Attention Networks for Single Image Super-Resolution—0
Multi-granularity Backprojection Transformer for Remote Sensing Image Super-Resolution—0
A Comparative Study on 1.5T-3T MRI Conversion through Deep Neural Network Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified