Astronomical multiband image fusion and restoration using pyramidal analysis and Markovian segmentation

This paper deals with noised astronomical multiband image fusion and restoration. The wavelet domain is well adapted for such tasks. In fact, intensity fluctuations corresponding to the noise are most important at the finest resolution and related details coefficients decrease quickly as the scale increases. Real structures in the image will therefore lead to larger detail coefficients values. This information can be suitably combined to detect and fuse real structures in all bands. In this work, we present a new fusion-restoration scheme for astronomical multispectral data, operating in four steps. At first, a pyramidal algorithm with one wavelet analyzes all spectral bands leading to a pyramid of details coefficients for each one. Secondly, the detail information for all wavelengths are combined in a unique multiband-multiresolution pyramidal to feed a hierarchical Markovian classifier based on a quadtree topology, providing a multiscale two classes segmentation map masking at each scale small enough coefficients. At third, the selected coefficients are merged leading to a unique pyramidal structure of detail coefficients. Finally, this resulting structure is used with an iterative reconstruction procedure to recover the denoised fused image. The Markovian quadtree models spatial, interscale and inter-band correlation together allowing a real whole cube information fusion.