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

arXiv: https://arxiv.org/abs/1905.05373 · HTML: https://mlsanity.com/paper/1905.05373/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2019.

## Built on by (most cited first, 7 of 7)

- [Advanced Underwater Image Quality Enhancement via Hybrid Super-Resolution Convolutional Neural Networks and Multi-Scale Retinex-Based Defogging Techniques](https://mlsanity.com/paper/2410.14285/) (2024)
- [Zoom, Enhance! Measuring Surveillance GAN Up-sampling](https://mlsanity.com/paper/2108.09285/) (2021)
- [Enhancement of Anime Imaging Enlargement using Modified Super-Resolution CNN](https://mlsanity.com/paper/2110.02321/) (2021)
- [CoT-MISR:Marrying Convolution and Transformer for Multi-Image Super-Resolution](https://mlsanity.com/paper/2303.06548/) (2023)
- [Towards Physics-informed Cyclic Adversarial Multi-PSF Lensless Imaging](https://mlsanity.com/paper/2407.06727/) (2024)
- [Enhanced hermit crabs detection using super-resolution reconstruction and improved YOLOv8 on UAV-captured imagery](https://mlsanity.com/paper/2408.03559/) (2024)
- [A super-resolution reconstruction method for lightweight building images based on an expanding feature modulation network](https://mlsanity.com/paper/2503.13179/) (2025)

## Abstract (arXiv)

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.