An investigation of image fusion algorithms using a visual performance-based image evaluation methodology

It is believed that the fusion of multiple different images into a single image should be of great benefit to Warfighters engaged in a search task. As such, more research has focused on the improvement of algorithms designed for image fusion. Many different fusion algorithms have already been developed; however, the majority of these algorithms have not been assessed in terms of their visual performance-enhancing effects using militarily relevant scenarios. The goal of this research is to apply a visual performance-based assessment methodology to assess four algorithms that are specifically designed for fusion of multispectral digital images. The image fusion algorithms used in this study included a Principle Component Analysis (PCA) based algorithm, a Shift-invariant Wavelet transform algorithm, a Contrast-based algorithm, and the standard method of fusion, pixel averaging. The methodology used has been developed to acquire objective human visual performance data as a means of evaluating the image fusion algorithms. Standard objective performance metrics, such as response time and error rate, were used to compare the fused images versus two baseline conditions comprising each individual image used in the fused test images (an image from a visible sensor and a thermal sensor). Observers completed a visual search task using a spatial-forced-choice paradigm. Observers searched images for a target (a military vehicle) hidden among foliage and then indicated in which quadrant of the screen the target was located. Response time and percent correct were measured for each observer. Results of this study and future directions are discussed.