استفاده از تخمين‌زننده كمينه‌كننده ميانگين مربعات خطا جهت بهبود كيفيت تصاوير مقطع نگاري رايانه‌اي از شكم بر اساس مدل مخلوط لاپلاس دومتغيره در حوزه تبديل ويولت مختلط چندبعدي
محورهای موضوعی : مهندسی برق و کامپیوتر
حسين رباني
1
(دانشگاه علوم پزشكي اصفهان)
منصور وفادوست
2
(دانشگاه صنعتي اميركبير)
کلید واژه: تبديل ويولت مختلطتخمين‌زننده كمينه‌كننده ميانگين مربعات خطامدل مخلوطمدل توأم,
چکیده مقاله :
يكي از مسايل تعيين‌كننده در زمينه حذف نويز در حوزه ويولت بر اساس تئوري بيز، استفاده از تابع چگال احتمال مناسب براي مدل‌كردن ضرايب ويولت مي‌باشد. از جمله خصوصيات ضرايب ويولت، وابستگي مابين مقياسي آنها مي‌باشد. در واقع مابين ضرايب ويولت و والد آنها در مقياس مجاور همبستگي بالايي وجود دارد. بر همين اساس در چند سال اخير به‌جاي استفاده از توزيع‌هاي تك‌متغيره در هر زيرباند ويولت از توزيع‌هاي دومتغيره استفاده شده است. در اين مقاله از مخلوط دو توزيع دومتغيره لاپلاسي براي مدل‌كردن ضرايب ويولت استفاده شده است. استفاده از اين مدل، هم خصوصيات نوك‌تيزبودن در مبدأ و دُم‌داربودن توزيع ضرايب ويولت و هم خصوصيت وابستگي مابين مقياسي آنها را به‌طور توأم به‌همراه خواهد داشت. بر اساس اين مدل توزيع احتمال و با استفاده از تخمين‌زننده كمينه‌كننده ميانگين مربعات خطا (MMSE)، الگوريتم تطبيقي جديدي براي كاهش نويز حاصل مي‌گردد. از اين الگوريتم براي كاهش نويز تصاوير مقطع نگاري رايانه‌اي (CT) از شكم در حوزه تبديل ويولت مختلط استفاده مي‌گردد. نتايج شبيه‌سازي‌هاي صورت‌گرفته با اين الگوريتم بيانگر بهبود عمليات كاهش نويز به‌طور كمي و كيفي در مقايسه با ديگر روش‌هاي مطرح‌شده در مقالات مي‌باشد.
One of the important subjects in the wavelet-based image denoising based on the Bayes theorem is choosing the appropriate density function for modeling the wavelet coefficients. The interscale dependency between parent and child coefficients is one of the statistical properties of wavelets. So, in the recent years instead of univariate distribution, bivariate density functions have been suggested by the researchers and in this paper we use a mixture of bivariate Laplacian densities for this reason. Using this distribution we are able to model both heavy-tailed property and interscale dependency of wavelets. Using the mentioned density function for a minimum mean squared error estimator, we obtain a new shrinkage function for denoising. Applying this function to each subband of discrete complex wavelet transform of abdominal computerized tomography images, we will be able to improve the quality of these images better than some reported methods.
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