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 2561–2570 of 3874 papers

TitleStatusHype
Omniscient Video Super-Resolution—0
Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolutionCode1
Best-Buddy GANs for Highly Detailed Image Super-ResolutionCode1
Training a Task-Specific Image Reconstruction Loss—0
D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-ResolutionCode1
Asymmetric CNN for image super-resolutionCode1
Zero-shot super-resolution with a physically-motivated downsampling kernel for endomicroscopy—0
Multi-frame Super-resolution from Noisy Data—0
Designing a Practical Degradation Model for Deep Blind Image Super-ResolutionCode1
JDSR-GAN: Constructing An Efficient Joint Learning Network for Masked Face Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified