De-noising and de-blurring of images using deep neural networks
Author(s)Fike, Amanda(Amanda J.)
Massachusetts Institute of Technology. Department of Mechanical Engineering.
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Deep Neural Networks (DNNs)  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)  in combination with BM3D , 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.
Thesis: S.B., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019Cataloged from PDF version of thesis.Includes bibliographical references (page 12).
DepartmentMassachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology