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Abstract 1.1 Digital Image Processing An image may be defined as a two-dimensional function J(i, D. where i and} are spatial (plane) coordinates, and the amplitude of J at any pair of coordinates (i, j) is called intensity or gray level of the image at that point. When i, } and the amplitude values are all finite, discrete quantities, then the image is called a digital image. The field digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. Theses elements are referred to as picture elements, image elements and pixels. Pixel is the term most widely used to denote the elements of digital image [3]. Digital image processing allows the use of much more complex algorithms for image processing, and hence, can offer both more sophisticated performance at simple tasks, and the implementation of methods which would be impossible by analog means. In particular, digital image processing is the only practical technology for: • Classification • Feature extraction • Pattern recognition • Projection • Multi-scale signal analysis 1.2 Image Denoising Image noise is the random vanation of brightness or color information in images produced by the sensor and circuitry of a scanner or digital camera. White noise is one of the most common problems in image processing. Even a high resolution photo is bound to have some noise in it. For a high-resolution photo a simple box blur may be sufficient, because even a tiny features like eyelashes or cloth texture will be represented by a large group of pixels. Unfortunately, this is not the case with video where real-time noise reduction is still a subject of many researches [4]. Image Denoising is one of the existing problems in research area. The need for efficient image restoration methods has grown with the massive production of digital images and movies of all kinds, often taken in poor conditions. No matter. |