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 2561–2570 of 3874 papers

TitleStatusHype
Single Image Super-Resolution Using Lightweight Networks Based on Swin Transformer—0
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network—0
Single image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction—0
Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network—0
Single Image Super-Resolution via Cascaded Multi-Scale Cross Network—0
Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis—0
Single Image Super Resolution via Manifold Approximation—0
Single Image Super-Resolution via Residual Neuron Attention Networks—0
Single Image Super Resolution - When Model Adaptation Matters—0
Single Image Super-resolution with a Switch Guided Hybrid Network for Satellite Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified