Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study

TLDR

Comparing image processing techniques is crucial for selecting appropriate algorithms, and image fusion is widely used for overlay, resolution refinement, and feature extraction in enhancement tasks. This study aims to compare image fusion methods relevant to geospatial and night‑vision applications. We evaluate 12 fusion metrics across six multiresolution fusion algorithms, two fusion schemes, and distorted inputs, providing a framework applicable to other algorithms and metrics. The results are linked to power‑spectrum and correlation‑based quality measures, summarizing contemporary techniques for objective assessment of image‑fusion algorithms.

Abstract

Comparison of image processing techniques is critically important in deciding which algorithm, method, or metric to use for enhanced image assessment. Image fusion is a popular choice for various image enhancement applications such as overlay of two image products, refinement of image resolutions for alignment, and image combination for feature extraction and target recognition. Since image fusion is used in many geospatial and night vision applications, it is important to understand these techniques and provide a comparative study of the methods. In this paper, we conduct a comparative study on 12 selected image fusion metrics over six multiresolution image fusion algorithms for two different fusion schemes and input images with distortion. The analysis can be applied to different image combination algorithms, image processing methods, and over a different choice of metrics that are of use to an image processing expert. The paper relates the results to an image quality measurement based on power spectrum and correlation analysis and serves as a summary of many contemporary techniques for objective assessment of image fusion algorithms.