Interferometric Phase Image Estimation via Sparse Coding in the Complex Domain

Hao Hongxing, Jose M. Bioucas-Dias, Vladimir Katkovnik

    Research output: Contribution to journalArticleScientificpeer-review

    35 Citations (Scopus)


    This paper addresses interferometric phase image estimation, i.e., the estimation of phase modulo-2 pi images from sinusoidal 2 pi-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 via matrix factorization with sparsity constraints on the code (i.e., the regression coefficients) enforced by the l(1) 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.

    Original languageEnglish
    Pages (from-to)2587-2602
    Number of pages16
    JournalIEEE Transactions on Geoscience and Remote Sensing
    Issue number5
    Publication statusPublished - May 2015
    Publication typeA1 Journal article-refereed


    • Dictionary learning (DL)
    • image similarity
    • interferometric phase estimation
    • online learning
    • phase estimation
    • phase unwrapping
    • sparse regression
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