Extraction of buried multidimensional signals and images in mixed sources of noise

In this paper, multi-dimensional extension and additional properties of already proposed extraction methods of buried one-dimensional signals in noise are developed. It is shown that heavy denoising uses no a-priori information, works without averaging or smoothing in the time or frequency domain with computation times much lower than those needed by ensemble averaging operations. Extraction is achieved independently of the nature of noise and locations of its spectral extent. Heavy denoising performances, comparative results with wavelets and other denoising algorithms, are illustrated via buried two-dimensional signals and images in noise. Proposed restoration of buried images in mixed sources of noise is able to preserve image information carried by fine structure, edges and texture. This ability opens novel perspectives for image restoration.