WAVELETS AND IMAGE FUSION - Image Processing, 1995. Proceedings., International Conference on

This paper describes an approach to image fusion using the wavelet transform. When images are merged in wavelet space, we can process different frequency ranges differently. For example, high frequency information from one image can be combined with lower frequency information from another, for performing edge enhancement. We have built a prototype system that allows experimentation with various wavelet array combination and manipulation methods for image fbsion, using a set of basic operations on wavelet frequency blocks. Problems caused by image misregistration and processing artifacts are described. Examples of wavelet fusion results are shown which merge a pair of images from different sensors. Lewis N. Graham, Metric Vision Madison, Alabama 1. RELATED WORK There has not been extensive work on image fusion using wavelets. One application which uses a similar approach of Gaussian pyramid decomposition for fusion of multiple coincident images is presented by Akerman [l]. Toet, Van Ruyven, and Valeton [2] describe the fusion of CCD and FLIR images using a ratio of low pass pyramid, which is similar to the Laplacian pyramid. Ranchin, Wald, and Mangolini [3] present a technique of enhancing the spatial resolution of a 20 meter multispectral SPOT image with the 10 meter panchromatic band from the same satellite. The technique they present is related to our work in that they apply multiresolution image decomposition and reconstruction using the wavelet transform. The key to Ranchin’s algorithm is to synthesize the missing frequency band for the multispectral image using the corresponding layer from the wavelet pyramid for the panchromatic images, then inverse transform. 2. OVERVIEW OF THE SYSTEM Our approach was to build a system that allows manipulation of the separate wavelet frequency blocks independently of each other. This allows us to emphasize different frequency ranges from different inputs in the output product. A wavelet transform array is synthesized for the product image and populated from the source images based on a set of predefined rules. After population, this synthetic array is inverse wavelet transformed to create the product image. Graham [4] provides a more detailed discussion of the application of wavelet theory to image fusion. Our prototype system handles images which are coregistered and the same size, with dimensions which are powers of two. A wavelet transform using the Daubechies [SI basis functions with filter lengths of 4, 12, or 20 is performed on all input images to be fused. A significant part of our work centered on determining rules to use in combining wavelet transform array information. The system needed to allow operations to be performed on individual wavelet array blocks, so that low and high frequency components could be treated differently. The system needed to enable the use of many different combination rules. The approach taken to achieve this was to identify several primitive operations that would be required to implement a variety of combination rules. These primitive operations act upon individual frequency blocks in wavelet arrays or on whole wavelet arrays at once. The most useful primitive operation is to simply take the coefficient with the maximum amplitude from any input image at each location in the wavelet transform array. Another operation is to average the values in all input wavelet transform arrays at each location. This operation, if performed on the entire wavelet array and inversetransformed, produces a result indistinguishable from the result of simply averaging the input images. We found