Bayesian anisotropic denoising in the Laguerre Gauss domain

  title={Bayesian anisotropic denoising in the Laguerre Gauss domain},
  author={Chiara Ercole and Patrizio Campisi and Alessandro Neri},
  booktitle={Electronic Imaging},
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… 

Circular Harmonic Functions: A unifying mathematical framework for image restoration, enhancement, indexing, retrieval and recognition

  • A. Neri
  • Computer Science
    2010 2nd European Workshop on Visual Information Processing (EUVIP)
  • 2010
This paper presents a review of the main properties of the Gauss-Laguerre Circular Harmonic Functions (CHF) particularly useful in image processing applications like restoration enhancement,



A Biologically Motivated Multiresolution Approach to Contour Detection

A biologically motivated multiresolution contour detection method using Bayesian denoising and a surround inhibition technique, relying on the observation that object contours lead to long connected components rather than to short rods obtained from textures.

A unified approach to boundary perception: edges, textures, and illusory contours

Experimental results demonstrate the performance of this model in detecting boundaries in real and synthetic images, and can be identified with processing by simple, complex, and hypercomplex cells in the visual cortex of mammals.

Image denoising using a local Gaussian scale mixture model in the wavelet domain

A maximum likelihood solution for estimating the hidden variable from an observation of the cluster of coefficients contaminated by additive Gaussian noise is developed and the estimated hidden variable is then used to estimate the original noise-free coefficients.

Characterization of Signals from Multiscale Edges

The authors describe an algorithm that reconstructs a close approximation of 1-D and 2-D signals from their multiscale edges and shows that the evolution of wavelet local maxima across scales characterize the local shape of irregular structures.

Low-complexity image denoising based on statistical modeling of wavelet coefficients

We introduce a simple spatially adaptive statistical model for wavelet image coefficients and apply it to image denoising. Our model is inspired by a recent wavelet image compression algorithm, the

Multiscale image features analysis with circular harmonic wavelets

In this contribution we introduce a new family of wavelets named Circular Harmonic Wavelets (CHW), suited for multiscale feature-based representations, that constitute a basis for general steerable

Contour Detection by Multiresolution Surround Inhibition

A multiresolution contour detector motivated by biological principles is proposed that is more effective than the classical hysteresis thresholding and robustness to noise is achieved by Bayesian gradient estimation.

Ideal spatial adaptation by wavelet shrinkage

SUMMARY With ideal spatial adaptation, an oracle furnishes information about how best to adapt a spatially variable estimator, whether piecewise constant, piecewise polynomial, variable knot spline,

Theory of edge detection

  • D. MarrE. Hildreth
  • Mathematics
    Proceedings of the Royal Society of London. Series B. Biological Sciences
  • 1980
The theory of edge detection explains several basic psychophysical findings, and the operation of forming oriented zero-crossing segments from the output of centre-surround ∇2G filters acting on the image forms the basis for a physiological model of simple cells.