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 3281–3290 of 3874 papers

TitleStatusHype
Light Field Super-resolution via Attention-Guided Fusion of Hybrid LensesCode0
A neural lens for super-resolution biological imaging—0
Super-Resolution Channel Estimation for Arbitrary Arrays in Hybrid Millimeter-Wave Massive MIMO Systems—0
Boosting Resolution and Recovering Texture of micro-CT Images with Deep Learning—0
DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal imagesCode0
Coupled-Projection Residual Network for MRI Super-Resolution—0
Hybrid Residual Attention Network for Single Image Super ResolutionCode0
Super-resolution meets machine learning: approximation of measures—0
Enhanced generative adversarial network for 3D brain MRI super-resolution—0
Gated Multiple Feedback Network for Image Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified