A Novel Pixel-Averaging Technique for Extracting Training Data from a Single Image, Used in ML-Based Image Enlargement

TL;DR

This paper introduces a pixel-averaging technique that extracts training data from a single image, enabling effective ML-based image enlargement without large datasets, particularly useful for medical images.

Contribution

The novel pixel-averaging algorithm allows training data extraction from a single image, reducing the need for extensive datasets in medical image upscaling tasks.

Findings

Relatively acceptable upscaling results compared to existing tools

Effective data extraction from a single image for ML training

Potential for further improvement with hyperparameter tuning

Abstract

Size of the training dataset is an important factor in the performance of a machine learning algorithms and tools used in medical image processing are not exceptions. Machine learning tools normally require a decent amount of training data before they could efficiently predict a target. For image processing and computer vision, the number of images determines the validity and reliability of the training set. Medical images in some cases, suffer from poor quality and inadequate quantity required for a suitable training set. The proposed algorithm in this research obviates the need for large or even small image datasets used in machine learning based image enlargement techniques by extracting the required data from a single image. The extracted data was then introduced to a decision tree regressor for upscaling greyscale medical images at different zoom levels. Results from the algorithm are relatively acceptable compared to third-party applications and promising for future research. This technique could be tailored to the requirements of other machine learning tools and the results may be improved by further tweaking of the tools hyperparameters.