Video Colour Denoising Using Fuzzy and Directional Techniques

Abstract

Different classes of filters have been proposed for removing noise from gray scale and colour images (Astola & Kuosmanen, 1997; Bovik, 2000; Kotropoulos & Pitas, 2001). They are classified into several categories depending on specific applications. Linear filters are efficient for Gaussian noise removal but often distort edges and have poor performance against impulsive noise. Nonlinear filters are designed to suppress noise of different nature, they can remove impulsive noise and guarantee detail preservation. Rank order based filters have received considerable attention due to their inherent outlier rejection and detail preservation properties (Astola & Kuosmanen, 1997; Bovik, 2000; Kotropoulos & Pitas, 2001, Plataniotis & Venetsanopoulos, 2000). In the last decade, many useful colour processing techniques based on vector processing have been investigated due to the inherent correlation that exists between the image channels compared to traditional component-wise approaches (Plataniotis & Venetsanopoulos, 2000). The fuzzy filters are designed by fuzzy rules to remove noise and to provide edge and fine detail preservation (Russo & Ramponi, 1994). The fuzzy filter depends on the fuzzy rules and the defuzzification process, which combines the effects of applied rules to produce an only output value (Russo & Ramponi, 2004, Schulte et al., 2007). The vector directional filters employ the directional processing taking pixels as vectors, and obtaining the output vector that shows a less deviation of its angles under ordering criteria in respect to the other vectors (Trahanias & Venetsanopoulos, 1996). This chapter presents the capability features of Fuzzy Directional (FD) filter to remove impulse noise from corrupted colour images (Ponomaryov, et al., 2010). The FD filter uses directional processing, where vectorial order statistics are employed, and fuzzy rules that are based on gradient values and angle deviations to determine, if the central pixel is noisy or present local movement. Simulation results in colour images and video sequences have shown that the restoration performance is better in comparison with other known filters. In Addition, we present the Median M-Type L(MML) filter for the removal of impulsive noise in gray-scale and colour image processing applications (Gallegos-Funes et al., 2008, ToledoLopez et al., 2008). The proposed scheme is based on modification of Lfilter that uses the MM (Median M-type) -estimator to calculate the robust point estimate of the pixels within the filtering window. The proposed filter uses the value of the central pixel within the filtering window to provide the preservation of fine details and the redescending M-