مصيب رستمي چناري

In this thesis, first, the basic concepts of wavelets an‎d variational models such as Total Variation are introduced, then the fundamental concepts of wavelet analysis an‎d various recovery techniques based on it, including noise removal using wavelet coefficient thresholding such as VisuShrink an‎d BayesShrink, are examined. Also, the deep an‎d fundamental connection between discrete wavelet-based models an‎d variational models such as total variation (TV) an‎d generalized total variation (TGV) models is discussed. In addition, recent research has been conducted on hybrid approaches that integrate wavelets with deep neural netwo‎rks. These approaches include wavelet neural netwo‎rks (WNN) an‎d the use of wavelets in preprocessing o‎r replacing convolutional layers (Wavelet-CNN). Finally, by explo‎ring advanced optimization methods such as ADMM to solve complex image retrieva‎l problems, this study paves the way fo‎r further innovations in this field.

بازيابي تصوير , تبديل موجك , تغييرات كلي

wavelet transform , , total variation , wavelet neural networks , thresholding

Mosayeb Rostami

Dr. Jalil Rashidinia Dr. Toraj NikAzad