1. Wavelet-based Image Fusion by Sitaram Bhagavathy Department of Electrical and Computer Engineering University of California, Santa Barbara Source: “Multisensor Image Fusion using the Wavelet Transform,” by H. Li, B.S. Manjunath, and S.K. Mitra; Graphical Models and Image Processing, May 1999.

2. Wavelet-based Image Fusion2 Outline Objective: To integrate complementary information from multisensor image data such that the new images are more suitable for –perception, feature extraction, segmentation, object recognition, etc. Wavelet-based fusion scheme: combines the DWTs of the input images and takes the inverse DWT Note: The input images have to be registered pixel- wise. The basic algorithm Modified feature selection algorithm Results and conclusion

3. Multiresolution Analysis

4. Wavelet-based Image Fusion4 The Basic Fusion Algorithm

5. Wavelet-based Image Fusion5 The Modified Algorithm Activity measure: Maximum absolute value in a window centered at each pixel Binary decision map created by maximum selection IDWT after consistency verification

6. Wavelet-based Image Fusion6 Fusion of Grayscale images Input 1Input 2 Output Note: I used 3 levels of decomposition, using the DB2 wavelet, for the experiments

7. Wavelet-based Image Fusion7 Fusion of Color Images I/P 1 I/P 2 O/P Orig -inal

8. Wavelet-based Image Fusion8 Conclusion Wavelet-based fusion methods give better results than Laplacian pyramid-based methods Fusion in the RGB color space works well but distorts the color at some pixels Fusion in the YUV color space did not give good results; needs more experimentation