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 2301–2310 of 3874 papers

TitleStatusHype
Mobile Computational Photography: A Tour—0
Content-aware Directed Propagation Network with Pixel Adaptive Kernel Attention—0
Translation position extracting in incoherent Fourier ptychography—0
Model Adaptation for Inverse Problems in Imaging—0
Content-adaptive Representation Learning for Fast Image Super-resolution—0
Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization—0
Model-Driven Channel Estimation for OFDM Systems Based on Image Super- Resolution Network—0
Constrained Diffusion Implicit Models—0
A Comprehensive Comparison of Projections in Omnidirectional Super-Resolution—0
Modeling Continuous Spatial-temporal Dynamics of Turbulent Flow with Test-time Refinement—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified