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 2421–2430 of 3874 papers

TitleStatusHype
Image Super-Resolution Using VDSR-ResNeXt and SRCGAN—0
Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy—0
Perception-oriented Single Image Super-Resolution via Dual Relativistic Average Generative Adversarial Networks—0
Image Super-Resolution With Deep Variational Autoencoders—0
Image Super-Resolution with Guarantees via Conformalized Generative Models—0
Image-to-image domain adaptation for vehicle re-identification—0
Image Transformer—0
IMDeception: Grouped Information Distilling Super-Resolution Network—0
Impact of deep learning-based image super-resolution on binary signal detection—0
Implicit Neural Representation Learning for Hyperspectral Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified