Recent developments in image acquisition literature have miniaturized the confocal laser endomicroscopes to improve usability and flexibility of the apparatus in actual clinical settings. However, miniaturized devices collect less light and have fewer optical components, resulting in pixelation artifacts and low resolution images. Owing to the strength of deep networks, many supervised methods known as super resolution have achieved considerable success in restoring low resolution images by generating the missing high frequency details. In this work, we propose a novel attention mechanism that, for the first time, combines 1st- and 2nd-order statistics for pooling operation, in the spatial and channel-wise dimensions.

Super-resolution, Confocal laser endo-microscopy, Image restoration

If you use our code, please cite our paper: Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy

The corresponding bibtex entry is:

@inproceedings{izadi2019image,
  title={Image super resolution via bilinear pooling: Application to confocal endomicroscopy},
  author={Izadi, Saeed and Sutton, Darren and Hamarneh, Ghassan},
  booktitle={International Workshop on Machine Learning for Medical Image Reconstruction},
  pages={236--244},
  year={2019},
  organization={Springer}
}