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 1191–1200 of 3874 papers

TitleStatusHype
Bilateral Network with Channel Splitting Network and Transformer for Thermal Image Super-Resolution—0
A mathematical theory of super-resolution and two-point resolution—0
Deep Unrolled Network for Video Super-Resolution—0
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs—0
Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction—0
Bi-GANs-ST for Perceptual Image Super-resolution—0
Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound—0
Deep Super-Resolution Network for Single Image Super-Resolution with Realistic Degradations—0
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution—0
Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified