De-noising and de-blurring of images using deep neural networks

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.

Name thesis:degree_name

Bachelor

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Fike, Amanda(Amanda J.)

Advisor dc:contributor.advisor

• George Barbastathis.

• Mechanical Engineering.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123266

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123266