Interactive multi-image blending for data visualisation and interpretation

Abstract

The ability to integrate data from a range of different images is often a crucial requirement for successful interpretation. Interactive multi-image blending is presented as a tool for facilitating the interpretation of complex information from multiple data sources. Traditionally, image blending has only been considered for cross-dissolving effects between two images. However, it is common for there to be more than just two images of interest in an interpretation task. We have developed a family of different multi-image blending tools to fill this need. These have been designed to support a number of different interpretation tasks and image types. For image blending to be a useful tool for multiple image interpretation it is important that the association between features and individual input images remain identifiable and distinct within the blend. We argue that interactivity of the blend is an important component for achieving this. Blending can also be usefully employed to interactively explore parameter variations for enhancement techniques. Often the best parameter values to use cannot be known beforehand, and it is common for different regions of an image to require different parameter values for best enhancement. By preparing a set of images processed over a sequence of scales and parameter values, and then interactively blending between these images, the interpretation of a data set can be greatly facilitated.