Interactive multi-image blending for visualization and interpretation

Peter Kovesi, Eun-Jung Holden, Jason Wong

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


The need to integrate information from images of different modalities is an increasingly common problem for the geosciences. 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. The emphasis of this work is on image blending for the effective visualization of data, rather than for attractive visual effects. To achieve this we have developed blending techniques that allow for the simultaneous presentation of more than two images. We present a family of different image blending techniques that support the blending of multiple images under a range of different situations. For image blending to be a useful tool for data interpretation it is important that the input images remain 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. © 2014 Elsevier Ltd.
Original languageEnglish
Pages (from-to)147-155
JournalComputers and Geosciences
Publication statusPublished - 2014


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