A comparative study of multi-focus, multi-resolution image fusion transforms and methods

Multi-resolution image decomposition transforms are a popular approach to current image processing problems such as image fusion, noise reduction, and deblurring. Over the past few decades, new algorithms have been developed based on the wavelet transform to remedy its directional and shift invariant shortcomings (undecimated discrete wavelet transform is shift invariant). This study provides a comprehensive analysis of multi-focus image fusion techniques using six different multi-resolution decomposition transforms to determine the optimal transform for an image fusion application. The transforms investigated are the wavelet, double-density wavelet, dual-tree wavelet, curvelet, contourlet, and bandelet. Furthermore, for each transform, seven transform coefficient fusion algorithms are analyzed and the performance is evaluated using eight no-reference objective image fusion metrics. The transforms and algorithms selected are applied to a data set that has 27 pairs of multi-focus source images used for image fusion. By bringing together the transforms, fusion algorithms, and metrics presented in this study as derived separately from different authors, the study seeks to compare these methods. However, a complete comparison amongst the different transforms, algorithms, and metrics has not been found in any of the existing literature. Our goal is to provide useful insight into their applications in image fusion. The summary of the aggregated results indicates that (1) the curvelet is the most robust transform, (2) down-up and linear are the most effect methods of fusion, and (3) Tsallis is the best metric for multi-focus image fusion.