UNIVERSITY OF BUCHAREST
FACULTY OF PHYSICS

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Conference: Bucharest University Faculty of Physics 2016 Meeting


Section: Biophysics; Medical Physics


Title:
Noise Reduction in Hyperspectral Data Using Wavelet Transform


Authors:
Dragos MANEA (1,2), Radu MUTIHAC (2)


Affiliation:
1) Nationale Institute of Research and Development for Optoelectronics – INOE 2000, Magurele, Romania

2) University of Bucharest, Faculty of Physics, Magurele, Romania


E-mail
dragos.manea@yahoo.com


Keywords:
Hyperspectral, denoise, wavelet transform, digital image


Abstract:
A digital image is a numerical representation of 1D, 2D, and 3D continuous images. The term "digital image" usually refers to raster or bitmap images. Pixels are the smallest individual elements in an image, holding values that represent the brightness of any given primary colour at any specific point. Typically, pixels are stored as a raster image or raster map, that is, an array of integers. These values are often transmitted or stored in a compressed form. Recently a more advanced technique called hyperspectral imaging has emerged combining the utility of a normal digital image and the spectral information provided by a spectrometer. The resulted image is a 3D matrix called hypercube containing the spatial x and y pixel coordinates (rows and columns) and spectral information of each pixel; this way a spectral plot can be obtained in each individual pixel. Being an optical technique, it strongly depends on the external conditions such as illumination, temperature, etc. These conditions can generate noise, more exactly, from the temperature difference between the environment and charge-coupled device (CCD) of the hyperspectral camera. Electrons are generated over time and independently of the light falling on the detector. They are captured by the CCD potential wells and counted, giving rise to a noise component added to the original signal. Methods of noise reduction are applied to suppress this effect. In this work, we are trying to "denoise" the signal by means of wavelet transform implemented in MATLAB, a novel technique that was not used so far. Preliminary results advocate in favour of this approach. In conclusion,an appropriately selected wavelet basis substantially reduces the noise in hyperspectral data.


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