Interactive multi-image blending for visualization and interpretation

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. HighlightsGeological interpretation requires integration of data from multiple sources.Interactive blending of two or more images facilitates interpretation.We present a series of image blending techniques designed for different applications.Interactivity is crucial for ensuring input images remain identifiable within a blend.Blending can be used to interactively explore parameter variations for enhancement.

[1]  Ron Brinkmann,et al.  The Art and Science of Digital Compositing , 1999 .

[2]  Ron Brinkmann The Art and Science of Digital Compositing, Second Edition: Techniques for Visual Effects, Animation and Motion Graphics (The Morgan Kaufmann Series in ... Morgan Kaufmann Series in Computer Graphics) , 2008 .

[3]  George Wolberg,et al.  Image morphing: a survey , 1998, The Visual Computer.

[4]  Peter Kovesi,et al.  Phase Preserving Tone Mapping of Non-Photographic High Dynamic Range Images , 2012, 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA).

[5]  F. J. Richards A Flexible Growth Function for Empirical Use , 1959 .

[6]  Tom Duff,et al.  Compositing digital images , 1984, SIGGRAPH.

[7]  David Borland,et al.  Rainbow Color Map (Still) Considered Harmful , 2007 .

[8]  Timo Ropinski,et al.  Multimodal Visualization with Interactive Closeups , 2009 .

[9]  Georgios Sakas,et al.  Data Intermixing and Multi‐volume Rendering , 1999, Comput. Graph. Forum.

[10]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[11]  David Williams,et al.  The arrangement of the three cone classes in the living human eye , 1999, Nature.

[12]  S. L. Loney Elements of coordinate geometry , 1895 .

[13]  Peter Kovesi,et al.  CET exSim: mineral exploration experience via simulation , 2013 .

[14]  S. Palmer Vision Science : Photons to Phenomenology , 1999 .

[15]  Neil A. Dodgson,et al.  Cross Dissolve Without Cross Fade: Preserving Contrast, Color and Salience in Image Compositing , 2006, Comput. Graph. Forum.

[16]  Stefan Bruckner,et al.  Information-based Transfer Functions for Multimodal Visualization , 2008, VCBM.

[17]  Michael H. Brill,et al.  Color appearance models , 1998 .