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14 figure 22 grayscale image 215 true color images a

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14 Figure 2.2 Grayscale image 2.1.5 True color Images A true color image is an image in which each pixel is specified by three values one each for the red, blue, and green components of the pixel‘s color. MATLAB store true color images as an m-by-n-by-3 data array that defines red, green, and blue color components for each individual pixel. True color images do not use a color map. The color of each pixel is determined by the combination of the red, green, and blue intensities stored in each color plane at the pixel‘s location. Graphics file formats store true color images as 24-bit images, where the red, green, and blue components are 8 bits each. This yields a potential of 16 million colors. The precision with which a real-life image can be replicated has led to the commonly used term true color image [10]. Example of color image
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15 Figure 2.3 Color Image. 2.2 Noise in an images The fundamental problem of image and signal processing is to effectively reduce noise from a digital color image while keeping its features intact (e.g., edges, color component distances, etc). The nature of the noise removal problem depends on the type of the noise corrupting the image. The two most commonly occurring types of noise are i) Additive noise (e.g. Gaussian and Impulse noise) and ii) Multiplicative noise (e.g. Speckle noise). Impulse noise is usually characterized by some portion of image pixels that are corrupted, leaving the remaining pixels unchanged. Examples of impulse noise are fixed-valued impulse noise and randomly valued impulse noise. We talk about additive noise when a value from a certain distribution is added to each image pixel, for example, a Gaussian distribution. Multiplicative noise is generally more difficult to remove from images than additive noise because the intensity of the noise varies with the signal intensity (e.g., speckle noise).In the literature several (fuzzy and non-fuzzy) filters have been studied for impulse noise reduction. Impulse noise is caused by errors in the data transmission generated in noisy sensors or communication channels, or by errors during the data capture from digital cameras. Noise usually quantified by the percentage of pixels which are corrupted. Corrupted pixels are either set to the maximum value or have single bits
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16 flipped over. In some cases, single pixels are set alternatively to zero or to the maximum value. This is the most common form of impulse noise and is called salt and pepper noise. Noise smoothing and edge enhancement are inherently conflicting processes, since smoothing a region might destroy an edge, while sharpening edges might lead to unnecessary noise [11]. 2.2.1Impulse noise in color images A color image can be represented via several color models such as RGB, CMY, CMYK, HIS, HSV and CIE L a* b*. Images, one for each primary color (Red, Green and Blue). Consider a color image represented in the x-y plane, then the third coordinate z= 1, 2, 3 will represent the color component of the image pixel at (x, y). Let f be the image function then f(x, y, 1) will represent the Red component of pixel at (x, y). Similarly, f(x, y, 2) and f(x, y, 3) represent the
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