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

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, recognition and retrieval. In fact, adoption of representations based on this family of functions, allows to reduce the computational complexity of Bayesian detectors and estimators. Applications to restoration and image retrieval are also discussed.

[1]  Kannan Ramchandran,et al.  Low-complexity image denoising based on statistical modeling of wavelet coefficients , 1999, IEEE Signal Processing Letters.

[2]  H H Arsenault,et al.  Properties of the circular harmonic expansion for rotation-invariant pattern recognition. , 1986, Applied optics.

[3]  Alessandro Neri,et al.  Maximum likelihood localization of 2-D patterns in the Gauss-Laguerre transform domain: theoretic framework and preliminary results , 2004, IEEE Transactions on Image Processing.

[4]  Alessandro Neri,et al.  Anisotropic wavelet thresholding for Bayesian image denoising , 2002, 2002 11th European Signal Processing Conference.

[5]  Alessandro Neri,et al.  Contour Detection by Multiresolution Surround Inhibition , 2006, 2006 International Conference on Image Processing.

[6]  Ramesh A. Gopinath,et al.  Enhancement of decompressed images at low bit rates , 1994, Optics & Photonics.

[7]  Emmanuel J. Candès,et al.  The curvelet transform for image denoising , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[8]  Alessandro Neri,et al.  Bayesian anisotropic denoising in the Laguerre Gauss domain , 2008, Electronic Imaging.

[9]  Alessandro Neri,et al.  Fuzzy Edge Enhancement in the Complex Wavelet Domain , 2009 .

[10]  Edward H. Adelson,et al.  Shiftable multiscale transforms , 1992, IEEE Trans. Inf. Theory.

[11]  William F. McGee,et al.  Complex Gaussian noise moments , 1971, IEEE Trans. Inf. Theory.

[12]  D Mendlovic,et al.  Wavelet-transform-based composite filters for invariant pattern recognition. , 1996, Applied optics.

[13]  Yasufumi Takama,et al.  Genetic algorithm-based relevance feedback for image retrieval using local similarity patterns , 2003, Inf. Process. Manag..

[14]  Alessandro Neri,et al.  Multiscale image features analysis with circular harmonic wavelets , 1995, Optics + Photonics.

[15]  S. Deans.  The Radon Transform and Some of Its Applications , 1983 .

[16]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[17]  Nicolai Petkov,et al.  Multilevel surround inhibition: a biologically inspired contour detector , 2007, Electronic Imaging.

[18]  Alessandro Neri,et al.  Adaptive Riesz basis decomposition for image search , 2009, 2009 17th European Signal Processing Conference.

[19]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

[20]  Alessandro Neri,et al.  3D Video Enhancement Based on Human Visual System Characteristics , 2010 .

[21]  Giovanni Jacovitti,et al.  Multiresolution circular harmonic decomposition , 2000, IEEE Trans. Signal Process..

[22]  Giovanni Jacovitti,et al.  Application of local Fisher information analysis to salient points extraction , 2008 .

[23]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Alessandro Neri,et al.  A Biologically Motivated Multiresolution Approach to Contour Detection , 2007, EURASIP J. Adv. Signal Process..

[25]  Stevan Rudinac,et al.  Global Image Search vs. Regional Search in CBIR Systems , 2007, Eighth International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS '07).

[26]  Alessandro Neri,et al.  Keypoints Selection in the Gauss Laguerre Transformed Domain , 2006, BMVC.

[27]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .