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 3281–3290 of 3874 papers

TitleStatusHype
Resolution enhancement in scanning electron microscopy using deep learning—0
Resolution Enhancement of Scanning Electron Micrographs using Artificial Intelligence—0
Resolution Invariant Autoencoder—0
RestoreDet: Degradation Equivariant Representation for Object Detection in Low Resolution Images—0
Rethinking Image Evaluation in Super-Resolution—0
Rethinking Image Super Resolution From Long-Tailed Distribution Learning Perspective—0
Rethinking Implicit Neural Representations for Vision Learners—0
Rethinking Super-Resolution as Text-Guided Details Generation—0
Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution—0
Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified