BLIND SEPARATION IN LOW FREQUENCIES USING WAVELET ANALYSIS, APPLICATION TO ARTIFICIAL VISION Danielle Nuzillard*, Sorin Curila* , **, Mircea Curila* , ** Laboratoire d'Automatique et de Microélectronique*, Université de Reims Champagne-Ardenne, Campus du Moulin de la Housse B.P. 1039, 51687 REIMS Cedex 2, FRANCE, Tel: +33.(0)3.26.91.82.16, Fax : +33.(0)3.26.91.31.06 University of Oradea**, 3700 Oradea, ROMANIA, str. Armatei Romane 5 e-mail: danielle.nuzillard@univ-reims.fr, sorin.curila@univ-reims.fr, mircea.curila@univ-reims.fr ABSTRACT We propose a method for image enhancement in colour word when a scattering environment reduces the vision. The main advantage of blind technique is that it does not require any a priori information about the scattering environment but supposes that the observed signals are linear mixtures of sources. Here, the natural logarithm of the degraded image provides an approximative additive mixture of reflectivity and transmittivity coefficients, the colour images provide three coloured mixtures (red, green, blue). They are processed by a Blind Source Separation (BSS) method in low spatial frequencies to display gray levels of pertinent features, which help one to vision enhancement. To display a cleaner vision, the set of mixtures is enriched thanks to classical signal processing technique. The chrominance information is restituted using post-processing techniques on HSV (Hue, Saturation, Value) space of degraded colour image. Experiments are made on images for which scattering environment is simulated in the laboratory. Keywords- Scattering environment, Artificial vision, Blind Source Separation, Second order blind identification, Independent component analysis, Wavelet denoising 1. INTRODUCTION The goal of computer vision is to develop techniques applied to artificial vision for which performance matches or exceeds that of human. Computer vision systems must include methods that enable applications for detecting objects through scattering environment, in bad weather conditions such as fog, rain, haze, snow, etc. The proposed topic is scientifically innovative because it associates the classical physics (radiative transfer in the scattering environment) to modern signal and image processing techniques as Blind Source Separation (BSS). The application of BSS methods for the improving the vision in fog conditions has for justification that the statistical properties of the scene are different from those of the environment between the scene and the observer. In the literature, the atmospheric propagation and the distribution of particles participating in effects such as light scattering correspond to an atmospheric model. We chosed to use a simple description that yields the properties of an actually model but only takes care of the image contrast degradation by surface radiation and atmospheric radiation phenomena. The simple model is described in section 2. To enhance the vision, we propose an approach presented in sections 3. The searched sources, using BSS methods, are the depth distribution (related to the transmittivity coefficient for an homogeneous scattering environment) and the reflectivity coefficient of the scene. The separation takes place in low frequencies domain due to environment distribution. Histogram equalization and its variations are very flexible since no assuming is made about the physics of the scene. But the results of these methods suggest that they are less effective in enhancing degraded images under scattering environment than model-based methods. The performances of the proposed method show a gain of 25% compared to the histogram equalization. Image processing techniques on HSV (Hue, Saturation, Value) space, proposed in section 4, complete the enhanced process to yield a colour image. Experimental results are available in section 5. An intense research activity falls out to describe the scattering environment properties and to enhance the image quality under such conditions. We mention here those whose aims are very close to our work. National Oceanic and Atmospheric Administration (NOAA) Science Center [19] catches images from two geostationary operational environmental satellites and combines them to create a product for the detection of fog and low status clouds at night. The "Fog Image" is obtained by a subtraction of the short-wave infrared window channel (3.9 micron wavelength) temperatures from the long-wave infrared window channel (10.7 micron) temperatures. The image is a special enhancement that estimates the thickness of a low cloud layer, based on the temperature difference between the two infrared channels. S. K. Nayar studies the visual manifestations of different weather conditions. Based on his observations, he develops models and methods for recovering pertinent scene properties such as three-dimensional structure from images taken under poor weather conditions [13]. D. Jackel presents a method for rendering of the atmosphere by means of Mie-scattering. The method is 77 4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), April 2003, Nara, Japan