A deconvolution technique using optimal Wiener filtering and regularization

Sekko, Edgard; Thomas, Gérard; Boukrouche, Abdelhani

doi:10.1016/s0165-1684(98)00161-3

Cited by 50 publications

(27 citation statements)

References 16 publications

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“…Moreover, deconvolution techniques have been developed to remove fluorescence from out-of-focus planes [31][32][33], thus enhancing image sharpness. Among the classical linear-deconvolution algorithms, the Wiener filter [34], and the Tikhonov-Miller filter [35] are widely used. The drawback of these linear filters is that they cannot restrict the solution domain with additional constraints, such as finite support, smoothness, regularization terms, or non-negativity.…”

Section: Image Acquisiɵonmentioning

confidence: 99%

Taking Aim at Moving Targets in Computational Cell Migration

Masuzzo

Troys

Ampè

et al. 2016

Trends in Cell Biology

122

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Cell migration is central to the development and maintenance of multicellular organisms. Fundamental understanding of cell migration can, for example, direct novel therapeutic strategies to control invasive tumor cells. However, the study of cell migration yields an overabundance of experimental data that require demanding processing and analysis for results extraction. Computational methods and tools have therefore become essential in the quantification and modeling of cell migration data. We review computational approaches for the key tasks in the quantification of in vitro cell migration: image pre-processing, motion estimation and feature extraction. Moreover, we summarize the current state-of-the-art for in silico modeling of cell migration. Finally, we provide a list of available software tools for cell migration to assist researchers in choosing the most appropriate solution for their needs.

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Section: Image Acquisiɵonmentioning

confidence: 99%

Taking Aim at Moving Targets in Computational Cell Migration

Masuzzo

Troys

Ampè

et al. 2016

Trends in Cell Biology

122

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show abstract

“…In this case, Piuri and Scotti [22] proposed a blur reduction module to decrease the blur effect. They implement combined two algorithms; including Lucy-Richardson [23] and Wiener filter [24] to restore the image. Moreover, they suggest also to implement blind deconvolution approach [25] if their method unable to handle the blur effect.…”

Section: Pre-processing and Image Enhancementmentioning

confidence: 99%

A review of fingerprint pre-processing using a mobile phone

Wan

2012

2012 International Conference on Wavelet Analysis and Pattern Recognition

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Nowadays, the mobile phones are becoming part of our daily life. They transformed from communication devices to personal computers. They are capable of storing personal information and connect to other applications where security is required. The need for securing the confidential information and the other application is increasing every day. Fingerprint authentication has been used in many different applications where high security is required. Mobile phones can process a lot of tasks and equipped with a built in camera capable of capturing images. A flngerprlnt authentication approach can successfully utilize the mobile phone cameras capturing flngerprlnt images as biometric traits. Unfortunately, the quality of images produced by mobile phone is very different from the images obtained by dedicated fingerprint sensors during the acquisition of human fingertips. In this paper, we present the current trend in flngerprint pre-processing techniques using mobile phones and explore some of the future possibilities in this field.

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“…In order for the reconstruction to be stable, the inverse filter should be regularized. It is wellknown, however, that in the case when the reconstruction is performed via linear filtering, such a regularized filter, which is also optimal in the MSE-sense, is the deconvolution Wiener filter [37]. Furthermore, in the case when both f(n) and u(n) behave as mutually independent white noises, this Wiener filter can be defined in the Fourier domain by (15) where ε is a regularization parameter (called inverse SNR).…”

Section: A Regularization Of Inverse Filteringmentioning

confidence: 99%

Blind Deconvolution of Medical Ultrasound Images: A Parametric Inverse Filtering Approach

Michailovich

Tannenbaum

2007

IEEE Trans. on Image Process.

124

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The problem of reconstruction of ultrasound images by means of blind deconvolution has long been recognized as one of the central problems in medical ultrasound imaging. In this paper, this problem is addressed via proposing a blind deconvolution method which is innovative in several ways. In particular, the method is based on parametric inverse filtering, whose parameters are optimized using two-stage processing. At the first stage, some partial information on the point spread function is recovered. Subsequently, this information is used to explicitly constrain the spectral shape of the inverse filter. From this perspective, the proposed methodology can be viewed as a "hybridization" of two standard strategies in blind deconvolution, which are based on either concurrent or successive estimation of the point spread function and the image of interest. Moreover, evidence is provided that the "hybrid" approach can outperform the standard ones in a number of important practical cases. Additionally, the present study introduces a different approach to parameterizing the inverse filter. Specifically, we propose to model the inverse transfer function as a member of a principal shift-invariant subspace. It is shown that such a parameterization results in considerably more stable reconstructions as compared to standard parameterization methods. Finally, it is shown how the inverse filters designed in this way can be used to deconvolve the images in a nonblind manner so as to further improve their quality. The usefulness and practicability of all the introduced innovations are proven in a series of both in silico and in vivo experiments. Finally, it is shown that the proposed deconvolution algorithms are capable of improving the resolution of ultrasound images by factors of 2.24 or 6.52 (as judged by the autocorrelation criterion) depending on the type of regularization method used.

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A deconvolution technique using optimal Wiener filtering and regularization

Cited by 50 publications

References 16 publications

Taking Aim at Moving Targets in Computational Cell Migration

Taking Aim at Moving Targets in Computational Cell Migration

A review of fingerprint pre-processing using a mobile phone

Blind Deconvolution of Medical Ultrasound Images: A Parametric Inverse Filtering Approach

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