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 3611–3620 of 3874 papers

TitleStatusHype
Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST—0
Denoising Prior Driven Deep Neural Network for Image RestorationCode0
Frame-Recurrent Video Super-Resolution—0
Light Field Super-Resolution using a Low-Rank Prior and Deep Convolutional Neural Networks—0
How should a fixed budget of dwell time be spent in scanning electron microscopy to optimize image quality?—0
Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks—0
High-throughput, high-resolution registration-free generated adversarial network microscopyCode0
Joint convolutional neural pyramid for depth map super-resolution—0
Aerial Spectral Super-Resolution using Conditional Adversarial Networks—0
On the Diversity of Realistic Image SynthesisCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified