Non rigid geometric distortions correction - Application to atmospheric turbulence stabilization

Mao, Yu; Gilles, Jérôme

doi:10.3934/ipi.2012.6.531

Cited by 55 publications

(33 citation statements)

References 20 publications

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“…), non-rigid transformations do not necessarily preserve straight lines as different sub-regions of images may move by differing vectors. Such non-rigid registration has many applications in imaging where either the object or recording process vary over time including remote sensing [17], medical imaging [18,19] and astronomy and photography [20]. In serial microscopy, the same challenges can be encountered, with dynamic sample behaviour and instrumental instabilities both contributing to erroneous data offsets.…”

Section: Non-rigid Registrationmentioning

confidence: 99%

Smart Align—a new tool for robust non-rigid registration of scanning microscope data

Jones

Yang

Pennycook

et al. 2015

Adv Struct Chem Imag

306

249

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Many microscopic investigations of materials may benefit from the recording of multiple successive images. This can include techniques common to several types of microscopy such as frame averaging to improve signal-to-noise ratios (SNR) or time series to study dynamic processes or more specific applications. In the scanning transmission electron microscope, this might include focal series for optical sectioning or aberration measurement, beam damage studies or camera-length series to study the effects of strain; whilst in the scanning tunnelling microscope, this might include biasvoltage series to probe local electronic structure. Whatever the application, such investigations must begin with the careful alignment of these data stacks, an operation that is not always trivial. In addition, the presence of low-frequency scanning distortions can introduce intra-image shifts to the data. Here, we describe an improved automated method of performing non-rigid registration customised for the challenges unique to scanned microscope data specifically addressing the issues of low-SNR data, images containing a large proportion of crystalline material and/or local features of interest such as dislocations or edges. Careful attention has been paid to artefact testing of the non-rigid registration method used, and the importance of this registration for the quantitative interpretation of feature intensities and positions is evaluated.

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Section: Non-rigid Registrationmentioning

confidence: 99%

Smart Align—a new tool for robust non-rigid registration of scanning microscope data

Jones

Yang

Pennycook

et al. 2015

Adv Struct Chem Imag

306

249

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“…Since the correction of videos degraded by turbulence has military [13,15] or sensitive [17] applications, the original sequences used to evaluate published methods are typically not available. In order to evaluate our online implementation we use two video sequences available in the literature from the publications by Efros et al [4] and Tian et al [21].…”

Section: Resultsmentioning

confidence: 99%

“…The centroid method for the correction of turbulence was introduced, in a slightly informal way, by Frakes-Monaco-Smith in 2001 [5] and it was formalized recently by Micheli [15]. There are other methods for the correction of turbulence based on optical flow [13]. A distinguishing feature of the centroid method is that it only computes flows between pairs of input images, and it never uses the average of the input images.…”

Section: Introductionmentioning

confidence: 99%

Implementation of the Centroid Method for the Correction of Turbulence

Meinhardt-Llopis

Micheli

2014

Image Processing on Line

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The centroid method for the correction of turbulence consists in computing the Karcher-Fréchet mean of the sequence of input images. The direction of deformation between a pair of images is determined by the optical flow. A distinguishing feature of the centroid method is that it can produce useful results from an arbitrarily small set of input images. Source CodeThe source code and a online demo are accessible at the IPOL web page of this article 1 .

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“…In [6], a linear Kalman filter approach on a pixel-by-pixel basis is introduced for predicting the restoration shift maps, where the average wander of each pixel of a static scenery over a certain period of time is assumed to be zero. Based on this, each pixel wander is modeled using a simple oscillator system and characterized by time-dependent differential equations, which is not always a practical assumption [11]. In a subsequent paper [7], a statistical approach is presented for estimating the covariance matrices required for Kalman filter based shift maps restoration.…”

Section: Introductionmentioning

confidence: 99%

A new pixel shiftmap prediction method based on Generalized Regression Neural Network

Halder

Tahtalı

Anavatti

2013

IEEE International Symposium on Signal Processing and Information Technology

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This paper proposes a new atmospheric warp estimation method based on Artificial Neural Network (ANN). We employed a Generalized Regression Neural Network (GRNN) for a-priori estimation of the upcoming warped frames using history of the previous frames. A non-rigid image registration technique is used for determining pixel shifts of the captured frames with respect to the reference frame. The proposed method is independent of the pixel-wander model. The performance of the method is evaluated using various quality metrics. Simulation results show that the proposed method provides substantial estimation of the upcoming frames with considerable errors.

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Non rigid geometric distortions correction - Application to atmospheric turbulence stabilization

Cited by 55 publications

References 20 publications

Smart Align—a new tool for robust non-rigid registration of scanning microscope data

Smart Align—a new tool for robust non-rigid registration of scanning microscope data

Implementation of the Centroid Method for the Correction of Turbulence

A new pixel shiftmap prediction method based on Generalized Regression Neural Network

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