Multi-Frame Super-Resolution
14 papers with code • 1 benchmarks • 3 datasets
When multiple images of the same view are taken from slightly different positions, perhaps also at different times, then they collectively contain more information than any single image on its own. Multi-Frame Super-Resolution fuses these low-res inputs into a composite high-res image that can reveal some of the original detail that cannot be recovered from any low-res image alone.
( Credit: HighRes-net )
BurstSR
Inter4K
PROBA-V
Most implemented papers
TR-MISR: Multiimage Super-Resolution Based on Feature Fusion With Transformers
Suanmd/TR-MISR
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2022
In addition, TR-MISR adopts an additional learnable embedding vector that fuses these vectors to restore the details to the greatest extent. TR-MISR has successfully applied the transformer to MISR tasks for the first time, notably reducing the difficulty of training the transformer by ignoring the spatial relations of image patches.
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignment
algolzw/bsrt
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To overcome the challenges in BurstSR, we propose a Burst Super-Resolution Transformer (BSRT), which can significantly improve the capability of extracting inter-frame information and reconstruction.
Open High-Resolution Satellite Imagery: The WorldStrat Dataset -- With Application to Super-Resolution
We hereby hope to foster broad-spectrum applications of ML to satellite imagery, and possibly develop from free public low-resolution Sentinel2 imagery the same power of analysis allowed by costly private high-resolution imagery.
Combination of Single and Multi-frame Image Super-resolution: An Analytical Perspective
Super-resolution is the process of obtaining a high-resolution image from one or more low-resolution images.
