Bayesian Fusion of Ensemble of Multifocused Noisy Images

This paper addresses the problem of simultaneous fusion and denoising of an ensemble of multifocused noisy source images using statistical approach. The central theme of the paper is to develop a novel generalized Bayesian framework based on maximum a posteriori (MAP) estimation technique to obtain the fused image from the noisy observations using a multiscale wavelet transform. A mathematically tractable multivariate a priori function is used in the MAP estimator to derive the closed-form expression of the fusion rule for the wavelet coefficients of noisy images. Experiments are carried out on a number of test-sets having an ensemble of multifocused source images with varying noise strengths to evaluate the performance of the proposed MAP-based fusion method as compared to the existing methods. Results show that the performance of the proposed method is better than that of the other wavelet or principal component analysis-based methods in terms of various metrics such as the structural similarity, peak signal-to-noise ratio and cross-entropy, uses of which are common both in the areas of fusion and denoising. In addition, the proposed method yields excellent results in terms of visual quality even in the case of non-Gaussian noise as well as computational load.