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 2051–2060 of 3874 papers

TitleStatusHype
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction—0
Learning From Unpaired Data: A Variational Bayes Approach—0
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution—0
Deep learning-based image super-resolution of a novel end-expandable optical fiber probe for application in esophageal cancer diagnostics—0
Deep-learning based down-scaling of summer monsoon rainfall data over Indian region—0
Learning Hierarchical Color Guidance for Depth Map Super-Resolution—0
Adaptive adversarial training method for improving multi-scale GAN based on generalization bound theory—0
Learning Image-Adaptive Codebooks for Class-Agnostic Image Restoration—0
Learning Implicit Generative Models by Matching Perceptual Features—0
Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified