A Comprehensive Survey on Synthetic Infrared Image synthesis

TL;DR

This survey reviews mathematical and deep learning methods for generating synthetic infrared images and targets, emphasizing their importance in applications like remote sensing and surveillance, and discusses future research directions.

Contribution

It provides a comprehensive overview of existing techniques for synthetic IR image generation and explores potential new methods to improve their efficiency and effectiveness.

Findings

Deep learning methods outperform traditional models in realism.

Synthetic IR data reduces the need for costly real data collection.

The survey identifies key challenges and future research directions.

Abstract

Synthetic infrared (IR) scene and target generation is an important computer vision problem as it allows the generation of realistic IR images and targets for training and testing of various applications, such as remote sensing, surveillance, and target recognition. It also helps reduce the cost and risk associated with collecting real-world IR data. This survey paper aims to provide a comprehensive overview of the conventional mathematical modelling-based methods and deep learning-based methods used for generating synthetic IR scenes and targets. The paper discusses the importance of synthetic IR scene and target generation and briefly covers the mathematics of blackbody and grey body radiations, as well as IR image-capturing methods. The potential use cases of synthetic IR scenes and target generation are also described, highlighting the significance of these techniques in various fields. Additionally, the paper explores possible new ways of developing new techniques to enhance the efficiency and effectiveness of synthetic IR scenes and target generation while highlighting the need for further research to advance this field.