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Speckle Noise Reduction with Statistical Modeling with Application in Medicine Implemented by AUT Researchers

 | Post date: 2020/06/15 | 
Morteza Ariyan a MSc graduate of computer engineering, artificial intelligence and the executor of “the reduction of image speckle noise using statistical modeling of the Shearlet conversion coefficients” said, ''in this project, we have specifically investigated the improvement of a specific type of these disorders (speckle noise)''.                                                                             
Referring to the purpose of the project, Morteza said, ''our specific purpose is to keep and increase clarity of the image edges as well as clarity of homogeneous areas of the image. For example, in medical images, the edge can be considered as the boundary between cancerous tumor and healthy tissue, and the homogenous area can act like a tumor tissue or a healthy tissue''.                                                                                                                   
He added, ''to achieve this purpose, we have used Shearlet as a tool for showing edges on the image better. More tangibly, it transfers this conversion of the image to a new space where two properties are evidently established''.                                                                                           
Referring to the completion of the project, he added ''the advantages of the method are that too much time and data is not needed to train the model''.                                                                 
It should be noted that this project was conducted under the supervision and guidance of Dr. Maryam Amir Mozleqani in the faculty of computer engineering