Bayesian anisotropic denoising in the Laguerre Gauss domain

Chiara Ercole; Patrizio Campisi; Alessandro Neri

doi:10.1117/12.768024

1 March 2008 Bayesian anisotropic denoising in the Laguerre Gauss domain

Chiara Ercole, Patrizio Campisi, Alessandro Neri

Proceedings Volume 6812, Image Processing: Algorithms and Systems VI; 681209 (2008) https://doi.org/10.1117/12.768024
Event: Electronic Imaging, 2008, San Jose, California, United States

Abstract

In this contribution, we propose an adaptive multiresolution denoising technique operating in the wavelet domain that selectively enhances object contours, extending a restoration scheme based on edge oriented wavelet representation by means of adaptive surround inhibition inspired by the human visual system characteristics. The use of the complex edge oriented wavelet representation is motivated by the fact that it is tuned to the most relevant visual image features. In this domain, an edge is represented by a complex number whose magnitude is proportional to its "strength" while phase equals the orientation angle. The complex edge wavelet is the first order dyadic Laguerre Gauss Circular Harmonic Wavelet, acting as a band limited gradient operator. The anisotropic sharpening function enhances or attenuates large/small edges more or less deeply, accounting for masking effects induced by textured background. Adapting sharpening to the local image content is realized by identifying the local statistics of natural and artificial textures like grass, foliage, water, composing the background. In the paper, the whole mathematical model is derived and its performances are validated on the basis of simulations on a wide data set.

Citation Download Citation

Chiara Ercole, Patrizio Campisi, and Alessandro Neri "Bayesian anisotropic denoising in the Laguerre Gauss domain", Proc. SPIE 6812, Image Processing: Algorithms and Systems VI, 681209 (1 March 2008); https://doi.org/10.1117/12.768024

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