Wavelet applied to computer vision in astrophysics

Albert Bijaoui; Eric Slezak; Myriam Traina

doi:10.1117/12.521039

27 February 2004 Wavelet applied to computer vision in astrophysics

Albert Bijaoui, Eric Slezak, Myriam Traina

Proceedings Volume 5266, Wavelet Applications in Industrial Processing; (2004) https://doi.org/10.1117/12.521039
Event: Photonics Technologies for Robotics, Automation, and Manufacturing, 2003, Providence, RI, United States

Abstract

Multiscale analyses can be provided by application wavelet transforms. For image processing purposes, we applied algorithms which imply a quasi isotropic vision. For a uniform noisy image, a wavelet coefficient W has a probability density function (PDF) p(W) which depends on the noise statistic. The PDF was determined for many statistical noises: Gauss, Poission, Rayleigh, exponential. For CCD observations, the Anscombe transform was generalized to a mixed Gasus+Poisson noise. From the discrete wavelet transform a set of significant wavelet coefficients (SSWC)is obtained. Many applications have been derived like denoising and deconvolution. Our main application is the decomposition of the image into objects, i.e the vision. At each scale an image labelling is performed in the SSWC. An interscale graph linking the fields of significant pixels is then obtained. The objects are identified using this graph. The wavelet coefficients of the tree related to a given object allow one to reconstruct its image by a classical inverse method. This vision model has been applied to astronomical images, improving the analysis of complex structures.

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Albert Bijaoui, Eric Slezak, and Myriam Traina "Wavelet applied to computer vision in astrophysics", Proc. SPIE 5266, Wavelet Applications in Industrial Processing, (27 February 2004); https://doi.org/10.1117/12.521039

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KEYWORDS

Wavelets

Image analysis

Image restoration

Wavelet transforms

Visual process modeling

Astronomy

Image processing

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