Optimization Techniques for Image Restoration

Many fields of study use images to make discoveries about the past, decisions for the present and predictions for the future. Images often acquire degradations such as a blur due to a patient moving during an x-ray or noise picked up through remote sensing imaging equipment. Images may also lose information through compression or</p><p> transmission. In this thesis, diffusion based models were used to solve the image restoration problem as these models can simultaneously remove noise, preserve edges and restore lost information. Specifically, numerical schemes were developed and tested for denoising via nonstandard diffusion that are more computationally efficient than the current method. Furthermore, a new model for digital inpainting is proposed based on the nonstandard diffusion model. Numerical results illustrate the effectiveness of both the denoising and inpainting models in image restoration.

Name thesis:degree_name

MS

Level thesis:degree_level

Immediate Access

Discipline thesis:degree_discipline

Computational Mathematics

Year dc:date.available

2004

Author dc:creator

• Pirolli, Melissa

Contributors dc:contributor

• Stacey Levine
• Constance D. Ramirez
• Eric Rawdon
• Kathleen Taylor

• denoising
• image processing
• image restoration
• inpainting
• nonstandard diffusion
• optimization

Language dc:language

English

Repository record dc:identifier

https://dsc.duq.edu/etd/1049

OAI identifier oai:identifier

oai:dsc.duq.edu:etd-2065