Traditional metrics for evaluating the efficacy of image processing techniques do not lend themselves to understanding the capabilities and limitations of modern image processing methods - particularly those enabled by deep learning. When applying image processing in engineering solutions, a scientist or engineer has a need to justify their design decisions with clear metrics. By applying blind/referenceless image spatial quality (BRISQUE), Structural SIMilarity (SSIM) index scores, and Peak signal-to-noise ratio (PSNR) to images before and after image processing, we can quantify quality improvements in a meaningful way and determine the lowest recoverable image quality for a given method.
@article{arxiv.1905.05373,
title = {Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)},
author = {Chris M. Ward and Josh Harguess and Brendan Crabb and Shibin Parameswaran},
journal= {arXiv preprint arXiv:1905.05373},
year = {2019}
}