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 2911–2920 of 3874 papers

TitleStatusHype
Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution—0
FireSRnet: Geoscience-Driven Super-Resolution of Future Fire Risk from Climate Change—0
Deep-learning based down-scaling of summer monsoon rainfall data over Indian region—0
Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition—0
Interpreting Super-Resolution Networks with Local Attribution Maps—0
Cryo-ZSSR: multiple-image super-resolution based on deep internal learning—0
On-Device Text Image Super Resolution—0
Recursive Deep Prior Video: a Super Resolution algorithm for Time-Lapse Microscopy of organ-on-chip experiments—0
Spectral Response Function Guided Deep Optimization-driven Network for Spectral Super-resolution—0
Fast and Robust Cascade Model for Multiple Degradation Single Image Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified