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 3451–3460 of 3874 papers

TitleStatusHype
Channel Splitting Network for Single MR Image Super-Resolution—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
Triple Attention Mixed Link Network for Single Image Super Resolution—0
MRI Super-Resolution using Multi-Channel Total VariationCode0
Recurrent Transition Networks for Character LocomotionCode2
Theory of Generative Deep Learning : Probe Landscape of Empirical Error via Norm Based Capacity Control—0
Towards WARSHIP: Combining Components of Brain-Inspired Computing of RSH for Image Super Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified