Bandelet-based image fusion: a comparative study for multi-focus images

There is a strong initiative to maximize visual information in a single image for viewing by fusing the salient data from multiple images. Many multi-focus imaging systems exist that would be able to provide better image data if these images are fused together. A fused image would allow an analyst to make decisions based on a single image rather than crossreferencing multiple images. The bandelet transform has proven to be an effective multi-resolution transform for both denoising and image fusion through its ability to calculate geometric flow in localized regions and decompose the image based on an orthogonal basis in the direction of the flow. Many studies have been done to develop and validate algorithms for wavelet image fusion but the bandelet has not been well investigated. This study seeks to investigate the use of the bandelet coefficients versus wavelet coefficients in modified versions of image fusion algorithms. There are many different methods for fusing these coefficients together for multi-focus and multi-modal images such as the simple average, absolute min and max, Principal Component Analysis (PCA) and a weighted average. This paper compares the image fusion methods with a variety of no reference image fusion metrics including information theory based, image feature based and structural similarity based assessments.