Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)
This provides a method for scientists and engineers to justify design decisions in image processing applications, though it is incremental as it applies existing metrics to a specific deep learning method.
The paper tackled the problem of evaluating image processing methods, specifically Super-Resolution Convolutional Neural Networks (SRCNN), by applying BRISQUE, SSIM, and PSNR metrics to quantify quality improvements and determine the lowest recoverable image quality.
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.