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 2101–2110 of 3874 papers

TitleStatusHype
Learning to Generate Images with Perceptual Similarity Metrics—0
Learning to Have an Ear for Face Super-Resolution—0
Learning to Super-Resolve Blurry Face and Text Images—0
Deep Attentive Generative Adversarial Network for Photo-Realistic Image De-Quantization—0
Toward task-driven satellite image super-resolution—0
Learning to synthesize: splitting and recombining low and high spatial frequencies for image recovery—0
Learning To Zoom Inside Camera Imaging Pipeline—0
Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation—0
Deep Artifact-Free Residual Network for Single Image Super-Resolution—0
Deep Appearance Models: A Deep Boltzmann Machine Approach for Face Modeling—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified