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 1151–1160 of 3874 papers

TitleStatusHype
Mining self-similarity: Label super-resolution with epitomic representationsCode0
Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problemsCode0
Deep Mean-Shift Priors for Image RestorationCode0
A Lightweight Recurrent Grouping Attention Network for Video Super-ResolutionCode0
Deeply-Recursive Convolutional Network for Image Super-ResolutionCode0
A Lightweight Image Super-Resolution Transformer Trained on Low-Resolution Images OnlyCode0
DeepLight: Reconstructing High-Resolution Observations of Nighttime Light With Multi-Modal Remote Sensing DataCode0
Benchmarking Probabilistic Deep Learning Methods for License Plate RecognitionCode0
MemNet: A Persistent Memory Network for Image RestorationCode0
Deep learning for temporal super-resolution 4D Flow MRICode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified