2026-09-21T01:48:33Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1232662021-07-05T14:03:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131024
George Barbastathis.
Fike, Amanda(Amanda J.)
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2019-12-13T18:58:25Z
2019-12-13T18:58:25Z
2019
2019
https://hdl.handle.net/1721.1/123266
1130062103
Thesis: S.B., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (page 12).
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.
by Amanda Fike.
S.B.
S.B. Massachusetts Institute of Technology, Department of Mechanical Engineering
12 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Mechanical Engineering.
De-noising and de-blurring of images using deep neural networks
Thesis
application/pdf
2019-12-13T18:58:23Z
Publication
Bachelor
MechE
unknown
unknown
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<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.</Abstract>
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