Investigation of Image Noise Reduction based on Unsupervised Learning

• Grant number: 22/14448-2
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: January 01, 2023
• End date: December 31, 2023
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Daniel Carlos Guimarães Pedronette
• Grantee: Murilo Magiolo Geraldini
• Host Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil
• Associated research grant: 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks, AP.JP2

Abstract

Noise consists of a degradation inherent in the acquisition process, in the most diverse types of imaging. In medical imaging, for example, this may impair or hamper image analysis by the radiologist. In addition, in the case of imaging systems employing ionizing radiation, such as Computed Tomography (CT), Mammography or Mammary Tomosynthesis, where the search for dose reduction should be somewhat constant according to the ALARA principle (As Low As Reasonably Achievable ), reducing dose leads to increased noise. Thus, considering also that noise can affect subsequent processing, such as segmentation or classification of images, evolving noise filtering techniques is of fundamental importance.Relevant techniques of noise filtering in images, such as Non Local Means (NLM) and Block Matching and 3D Filtering (BM3D), are used in a non-local approach, ie, the similarity between regions (patches) of the image (more than between unit pixels) to perform the filtering. In this sense, the choice of the similarity measure and the technique of selection of more similar patches are essential for an adequate performance of these methods. In this work, we intend to investigate the use of unsupervised learning approaches for replacing similarity measures in order to improve the effectiveness of noise filtering tasks.