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 1561–1570 of 3874 papers

TitleStatusHype
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations—0
A Coordinate Descent Approach to Atomic Norm Denoising—0
Advancing Supervised Local Learning Beyond Classification with Long-term Feature Bank—0
2D Neural Fields with Learned Discontinuities—0
Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings—0
Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution—0
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain—0
CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network—0
HyperINR: A Fast and Predictive Hypernetwork for Implicit Neural Representations via Knowledge Distillation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified