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 3471–3480 of 3874 papers

TitleStatusHype
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and EnhancementCode0
Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution—0
Channel Splitting Network for Single MR Image Super-Resolution—0
Deep learning-based super-resolution in coherent imaging systems—0
Deep Learning-Based Channel EstimationCode0
Efficient Two-Dimensional Line Spectrum Estimation Based on Decoupled Atomic Norm Minimization—0
Deep Bi-Dense Networks for Image Super-ResolutionCode0
Image Super-Resolution Using VDSR-ResNeXt and SRCGAN—0
MRI Super-Resolution using Multi-Channel Total VariationCode0
Triple Attention Mixed Link Network for Single Image Super Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified