# Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)

## TL;DR

This paper evaluates the effectiveness of Super-Resolution CNNs in image quality enhancement using traditional metrics like BRISQUE, SSIM, and PSNR, highlighting their limitations and capabilities.

## Contribution

It introduces a framework for assessing super-resolution methods with standard quality metrics to understand their efficacy and limitations.

## Key findings

- BRISQUE, SSIM, and PSNR effectively quantify image quality improvements.
- Metrics help determine the lowest recoverable image quality.
- Evaluation reveals strengths and limitations of SRCNN.

## Abstract

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.