عنوان البحث(Papers / Research Title)
proposed optimal method for GIS image noise removal
الناشر \ المحرر \ الكاتب (Author / Editor / Publisher)
توفيق عبد الخالق عباس الاسدي
Citation Information
توفيق,عبد,الخالق,عباس,الاسدي ,proposed optimal method for GIS image noise removal , Time 21/05/2012 06:43:25 : كلية تكنولوجيا المعلومات
وصف الابستركت (Abstract)
proposed optimal method for GIS image noise removal
الوصف الكامل (Full Abstract)
Abstract :
Traditional Noise removal methods are depending on studying one method such as winner filter. This method yield to noise analysis with different intensity for the same method. The aim of this method is to detect area that makes filter working on, so the noise removal depending on this way is not useful for above reason.The suggest method deals with knowing the noise distribution and extracting Features of this noise and selects the best way to remove the noise, with using all possible knowledge. This method benefits from the traditional methods which classify noise to multi level depending on it intensity.In Noise distribution, we depend on statistic methods according to goodness of fitting (Gof). This distribution has two phases, the first one is hypothesis and the second is statistic test. The hypothesis phase assuming normal distribution. We accept this hypothesis if it is satisfied with the condition. Gof uses different methods including chi-square, Kolmogorov-Smirnov, and moments. Features extracting for additive noise are including two types; block –base and filter base. Block-base includes dividing image to small blocks then selects the best homogenous one to extract features from this block. Filter-base is an applied filter on image (laplace and sobel) and by using threshold to remove all details of image to compute the average. For impulse noise we are uses noise definition. In Geographic Information Systems (GIS), we applied the suggested methods by using data base information as feature in each step.
1- introduction
Image processing is a rapidly growing area of computer science. Its growth has been fueled by technological advances in digital imaging, computer processors and mass storage devices. Fields which traditionally used analog imaging are now switching to digital systems, for their flexibility and affordability. Important examples are medicine, film and video production, photography, GIS , remote sensing, and security monitoring[1] . Digital image processing is concerned primarily with extracting useful information from images. Ideally, this is done by computers, with little or no human intervention. Image processing algorithms may be placed at three levels. At the lowest level are those techniques which deal directly with the raw, possibly noisy pixel values, with denoising and edge detection. In the middle are algorithms which utilize low level results for further means, such as segmentation and edge linking. At the highest level are those methods which attempt to extract semantic meaning from the information provided by the lower levels, for example, handwriting recognition [2] Image restoration is distinct from image enhancement techniques, which are designed to manipulate an image in order to produce results more pleasing to an observer, without making use of any particular degradation models. Image enhancement refers to the techniques by which we try to improve an image such that it looks subjectively better by improving the visual appearance of the image [3] GIS are commonly defined as an information system that manages, manipulates, and analyzes spatial data. GIS are used to produce information that is useful in decision making.[4]. a GIS is based on a structured database that describes the world in geographic terms, database . A GIS is a unique kind of database of the world—a geographic database (geodatabase), As part of a GIS geodatabase design, users specify how certain features will be represented of Ordered collections of vector-based features (sets of points, lines, and polygons), Raster data sets, networks,Terrains and other surfaces and Survey data sets.. GIS data sets include traditional tabular attributes that describe the geographic objects. These tabular information sets and relationships play a key role in GIS data models, just as they do in traditional database applications [5].
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