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 3121–3130 of 3874 papers

TitleStatusHype
Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution and Matcher Fusion—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
An Interpretation of Regularization by Denoising and its Application with the Back-Projected Fidelity Term—0
An Image Analogies Approach for Multi-Scale Contour Detection—0
Single Image Super-Resolution via Cascaded Multi-Scale Cross Network—0
Angle of Arrival Estimation with Transformer: A Sparse and Gridless Method with Zero-Shot Capability—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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified