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429 results on '"Katkovnik, Vladimir"'

174. Interferometric Phase Image Estimation via Sparse Coding in the Complex Domain.

Author

Hao Hongxing, Bioucas-Dias, José M., and Katkovnik, Vladimir

Subjects
INTERFEROMETRY, IMAGE processing, ESTIMATION theory, IMAGE compression, IMAGE reconstruction
Abstract

This paper addresses interferometric phase image estimation, i.e., the estimation of phase modulo-2π images from sinusoidal 2π-periodic and noisy observations. These degradation mechanisms make interferometric phase image estimation a quite challenging problem. We tackle this challenge by reformulating the true estimation problem as a sparse regression, often termed sparse coding, in the complex domain. Following the standard procedure in patch-based image restoration, the image is partitioned into small overlapping square patches, and the vector corresponding to each patch is modeled as a sparse linear combination of vectors, termed the atoms, taken from a set called dictionary. Aiming at optimal sparse representations, and thus at optimal noise removing capabilities, the dictionary is learned from the data that it represents viamatrix factorization with sparsity constraints on the code (i.e., the regression coefficients) enforced by the l1 norm. The effectiveness of the new sparse-coding-based approach to interferometric phase estimation, termed the SpInPHASE, is illustrated in a series of experiments with simulated and real data where it outperforms the state-of-the-art. [ABSTRACT FROM AUTHOR]

Published
2015
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179. Multiple Plane Phase Retrieval Based On Inverse Regularized Imaging and Discrete Diffraction Transform.

Author

Migukin, Artem, Katkovnik, Vladimir, and Astola, Jaakko

Subjects
*OPTICAL diffraction, *DIGITAL image processing, *ALGORITHMS, *DETECTORS, *HOLOGRAPHY
Abstract

The phase retrieval is formulated as an inverse problem, where the forward propagation is defined by Discrete Diffraction Transform (DDT) [1], [2]. This propagation model is precise and aliasing free for pixelwise invariant (pixelated) wave field distributions in the sensor and object planes. Because of finite size of sensors DDT can be ill-conditioned and the regularization is an important component of the inverse. The proposed algorithm is designed for multiple plane observations and can be treated as a generalization of the Gerchberg-Saxton iterative algorithm. The proposed algorithm is studied by numerical experiments produced for phase and amplitude modulated object distributions. Comparison versus the conventional forward propagation models such as the angular spectrum decomposition and the convolutional model used in the algorithm of the same structure shows a clear advantage of DDT enabling better accuracy and better imaging. [ABSTRACT FROM AUTHOR]

Published
2010
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