  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. We compare the efficacy of our method
to 11 other existing single image super resolution techniques that compensate
for the reduction in image quality caused by the necessity of endomicroscope
miniaturization. All evaluations are carried out on three publicly available
datasets. Experimental results show that our method can produce competitive
results against state-of-the-art in terms of PSNR, SSIM, and IFC metrics.
Additionally, our proposed method contains small number of parameters, which
makes it lightweight and fast for real-time applications.
