Penalized-likelihood image reconstruction for digital holography.
TLDR
A new numerical reconstruction approach using a statistical technique that reconstructs the complex field of the object from the real-valued hologram intensity data and derives an optimization transfer algorithm that monotonically decreases the cost function at each iteration.Abstract:
Conventional numerical reconstruction for digital holography using a filter applied in the spatial-frequency domain to extract the primary image may yield suboptimal image quality because of the loss in high-frequency components and interference from other undesirable terms of a hologram. We propose a new numerical reconstruction approach using a statistical technique. This approach reconstructs the complex field of the object from the real-valued hologram intensity data. Because holographic image reconstruction is an ill-posed problem, our statistical technique is based on penalized-likelihood estimation. We develop a Poisson statistical model for this problem and derive an optimization transfer algorithm that monotonically decreases the cost function at each iteration. Simulation results show that our statistical technique has the potential to improve image quality in digital holography relative to conventional reconstruction techniques.read more
Citations
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Journal ArticleDOI
Least-squares based inverse reconstruction of in-line digital holograms
Edwin N. Kamau,Nicholas Burns,Claas Falldorf,Christoph von Kopylow,John Watson,Ralf B. Bergmann +5 more
TL;DR: It is demonstrated that by reformulating the reconstruction problem as an inverse problem and by integrating a contour gradient based auto-focus search algorithm into the reconstruction routine, a more fundamental solution for the inversion of a hologram can be attained.
Book ChapterDOI
Digital Hologram Processing in On-Axis Holography
TL;DR: In this article, a numerical reconstruction of digital holograms is presented, which is based on inverse problems and Cramer-Rao lower bounds are derived from the hologram formation model.
Design and Evaluation of Feature Interaction Filtering with Use Case Maps
TL;DR: A new FI filtering method consisting of two phases is proposed, which showed that the FI-prone scenarios obtained successfully covered all scenarios that lead to actual FIs, which implies that 90% reduction of the cost for the scenario investigation was achieved.
Proceedings ArticleDOI
Simplified digital holographic reconstruction using statistical methods
TL;DR: A new penalized-likelihood approach based on the measurement statistics and edge-preserving regularization is proposed for reconstructing a complex object wavefront from digital holograms and a new simplified iterative algorithm is derived that monotonically decreases the cost function.
Journal ArticleDOI
Fast Spatial Resolution Analysis of Quadratic Penalized Least-Squares Image Reconstruction With Separate Real and Imaginary Roughness Penalty: Application to fMRI
TL;DR: A fast and stable calculation is proposed that also uses FFT-based calculations to approximate the local impulse responses of the real and imaginary parts of a quadratic image reconstruction of fMRI data that uses separate roughness penalties for the realand imaginary parts.
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