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 3111–3120 of 3874 papers

TitleStatusHype
Facial Attribute Capsules for Noise Face Super Resolution—0
Video Face Super-Resolution with Motion-Adaptive Feedback Cell—0
Multi-Level Feature Fusion Mechanism for Single Image Super-Resolution—0
Reconstructing the Noise Manifold for Image Denoising—0
Multiple Angles of Arrival Estimation using Neural Networks—0
Super-resolution of multispectral satellite images using convolutional neural networks—0
Large Hole Image Inpainting With Compress-Decompression Network—0
Learning Deep Analysis Dictionaries -- Part II: Convolutional Dictionaries—0
A Generative Adversarial Network for AI-Aided Chair Design—0
Learning Deep Analysis Dictionaries for Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified