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We propose an image fusion method robust to misaligned source images based on their multiscale edge representations. Significant long edge curves at the second scale are selected to decide edge locations at each scale for the multiscale edge representations of source images. Then, processes are only executed on the representations that contain the main spatial structures of the images and also help suppress noise interference. A registration process is embedded in our fusion method. Edge correlation, calculated at the second scale, is involved as a match measure determining the fusion rules and also as a similarity measure quantifying the matching extent between source images, which makes the registration and fusion processes share the same data and hence lessens the computation of our method. Experimental results prove that, no matter whether in a noiseless or noisy condition, the proposed method provides satisfying treatment to misregistered source images and behaves well in terms of visual and objective evaluations on the fusion results, which further verifies the robustness of our edge-based method to misregistration and noise.

Considering the reality of image formation as well as the statistical characteristics of contourlet coefficients, we propose a method for statistical image fusion in the contourlet domain. For high-frequency subbands, an image formation model was established that has a continuous-valued blur factor to reflect the actual imaging situation. According to this model, fused contourlet coefficients were estimated by use of an expectation-maximization (EM) algorithm. During the estimation, a contourlet hidden Markov tree model was adopted to grasp all dependencies among coefficients and aim for better estimation results. Because the blur factor is a continuous variable, we exploited an explicit expression to update the factor in the EM algorithm, which contributed to a decline in the number of iterations for convergence. Experimental results indicated that, especially for multifocus images, the proposed method provides more satisfying fusion results in terms of visual effects and objective evaluations than some typical fusion methods based on multiscale decomposition and some statistical fusion methods using a discrete blur factor.