De-noising and de-blurring of images using deep neural networks
Name
1130062103-MIT.pdf
Size
2.04 MB
Format
Adobe PDF
Checksum (MD5)
bb1cda32b703e7dceeab10aa61a93376
Author(s)
Fike, Amanda(Amanda J.)
Advisor(s)
George Barbastathis.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
Deep Neural Networks (DNNs) [1] are often used for image reconstruction, but perform better reconstructing the low frequencies of the image than the high frequencies. This is especially the case when using noisy images. In this paper, we test using a Learning Synthesis Deep Neural Network (LS-DNN) [2] in combination with BM3D [3], an off the shelf de-noising tool, to generate images, attempting to decouple the de-noising and de-blurring steps to reconstruct noisy, blurry images. Overall, the LS-DNN performed similarly to the DNN trained only with respect to the ground truth images, and decoupling the de-noising and de-blurring steps underperformed compared to the results of images de-blurred and de-noised simultaneously with a DNN.
Description
Thesis: S.B., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (page 12).
Subjects
Mechanical Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Persistent DSpace Link
