المويجات الهندسية المكيفة وتطبيقاتها في ازالة التشويش للصور الرقمية == Adaptive Geometrical Wavelets And Their Applications In Digital Image De - Noising
Author name:
عامر محمد نوري المهداوي
Supervisor name:
ظافر حسين رشيد النجار
General topic:
Administration and Economics
Specific topic:
Statistics
Degree:
Doctorate
University:
University of Baghdad - Faculty Of Administration And Economics - Department Of Statistics
Language:
Arabic
University location:
Baghdad
First pages:
07T3820 - p.pdf
Abstract:
تختلف الاهمية النسبية للمعلومات المرئية المحتواة في الصور. اذ ان البحوث الحديثة في فسلجة الرؤية اثبتت بان الدماغ البشري يتحسس لحواف مكونات الصورة بالدرجة الاولى، اما الانسجة فتاتي بالدرجة الثانية من الاهمية. دفع هذا الامر العديد من الباحثين في حقل المعالج | The importance of visual information contains in images are relatively deferent. Recent researches in psychology of vision have proven that human brain senses the edges of image objects in the first order, where image textures comes in the second order of importance. This problem had motivated researchers in the field of image processing towards finding new efficient methods that work on eliminating less important visual information which gives as a result a brief description containing the most important information. This description serves two domains : the first one is image compression, where it is possible to reduce the amount of data used in representation, which helps in reducing the required transmission time and also needs less storage size. The second one is image denoising, by reducing the importance of noisy data which helps in eliminating the noise, or at least compress its effect.Recently, it has become evident that separable transforms, such as wavelets, are not necessarily best suited for image representation due to their disability of catching line discontinuities that represent the objects edges, which make them unsuitable in giving adequate description for deferent geometrical shapes of these edges. This led to the appearance of the geometrical wavelets which has been proven its superior to nearly all of the classical wavelets in giving higher specific approximations and much more economical in data size of the image. These functions are non - separable and they are divided into two parts : adaptive functions and non - adaptive functions.This dissertation focuses on studying the first and most important function of the family of adaptive geometrical wavelets functions, the one called wedgelets, in addition to propose generalizations to it in order to make it more flexible in catching deferent curved shapes of image edges, which improves its performance in providing much more specific approximations depending on less number of coefficients. This work emphasizes basically in image denoising. And in order to increase the applicable importance of these functions, it has been suggested two methods to estimate the noise level that effects the image, and eventually choosing the best approximation according to this estimation.Finally, a comparison is made between the proposed approximation methods and the classical ones by applying on several tested images that have deferent properties, through the simulation of exposing them with deferent levels of noise. The results of this comparison shows good size of improvement in the performance achieved by the addition of these proposed functions.
