Optimizing the number of decomposition levels for wavelet-based multifocus image fusion

The performance of wavelet-based multifocus image fusion depends on the number of decomposition levels in the wavelet transform. Too few decomposition levels result in poor spatial quality in the fused images. On the other hand, too many levels induce distortion between the original and the fused images. In general, fusion of images with larger resolution requires a higher number of decomposition levels. In this paper, a multiobjective optimization approach to determine the optimal number of decomposition levels is presented. First the optimization flow of decomposition levels for wavelet-based multifocus image fusion is presented; then the proper evaluation metrics for multifocus image fusion are given, new conditional mutual information is proposed, which can avoid the information overlapping; and finally the algorithm of multiobjective particle swarm optimization using the local best model is designed to search the optimal number of decomposition levels. In the experiments it is found that the optimal decomposition level is not a fixed value, but rather, changes with the characteristics of the original images. The experimental results also show that using the method of multiobjective optimization can effectively obtain the optimal number of decomposition levels.