A Kalman Filter Approach for Denoising and Deblurring 3-D Images by Multi-view Data

This paper introduces a novel multi-view deconvolution technique for 3-D images. An optimal Kalman-based minimum variance restoration algorithm is allowed to combine a series of image samples acquired from different viewing directions. The extended algorithm is based on the definition of a stochastic state-space representation of the image, which embeds the description of blurring effects and noise disturbances. The consistency of this model gives guarantee for high restoration performances. The extension to the data fusion is obtained by suitably including the multi-view acquisition procedure within the representation. The final algorithm results to be effective for improving the resolution and the isotropy of the estimated image, as shown by the reported numerical results.