Print Email Facebook Twitter Phase Diversity for Blind Multi-Frame Deconvolution Title Phase Diversity for Blind Multi-Frame Deconvolution Author Vrijburg, Dominique (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Delft Center for Systems and Control) Contributor Verhaegen, M.H.G. (mentor) Soloviev, O.A. (mentor) Myers, N.J. (graduation committee) Degree granting institution Delft University of Technology Programme Mechanical Engineering | Systems and Control Date 2021-07-09 Abstract When objects are imaged, often aberrations slip into the captured images. Many techniques exist to remove part of those aberrations using hardware, software or a combination of those. This research will focus on Phase Diversity for Blind Multi-Frame Deconvolution. In Blind Multi-Frame Deconvolution both the object and the aberrations are estimated from multiple images of the same object. Previous research has shown, Phase Diversity can be used to improve the quality of the reconstructed object in combination with Blind Multi-Frame Deconvolution. Using Defocus Phase Diversity is currently mainly used in literature.In this research new Phase Diversity methods are proposed. When there is no knowledge on the object or aberrations, Phase Diversities are proposed to improve the object reconstruction compared to Defocus Phase Diversity.We also propose a new method to optimize the Phase Diversity for the N + 1th image, when already N images have been taken. This method first determines the low values in the frequency spectra of the images. Next it estimates a Phase Diversity that results in a Modulation Transfer Function to enhances those frequencies. This method leads to an improved restoration quality in terms of Structural Similarity and Peak Signal to Noise Ratio when there is Gaussian noise or Poisson noise with a higher Signal to Noise Ratio. Recommendations aredone to improve the method for lower Signal to Noise Ratios. Subject Blind Multi-Frame DeconvolutionPhase DiversityImaging To reference this document use: http://resolver.tudelft.nl/uuid:efe98076-ff2e-4cae-93f7-4966d3bed05e Part of collection Student theses Document type master thesis Rights © 2021 Dominique Vrijburg Files PDF Thesis_8_.pdf 3.93 MB Close viewer /islandora/object/uuid:efe98076-ff2e-4cae-93f7-4966d3bed05e/datastream/OBJ/view