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Patch-Based Non-Local Functional for Denoising Fluorescence Microscopy

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Patch-Based Non-Local Functional for Denoising Fluorescence Microscopy 1 Patch-based non-local functional for denoising fluorescence microscopy image sequences Jérôme Boulanger, Charles Kervrann, Patrick Bouthemy, Peter Elbau, Jean-Baptiste Sibarita, Jean Salamero Abstract—We present a non-parametric regression quence denoising, patch-based approach, Poisson noise, method for denoising 3D image sequences acquired via variance stabilization, adaptive estimation, energy mini- fluorescence microscopy. The proposed method exploits mization. the redundancy of the 3D+time information to improve the signal-to-noise ratio of images corrupted by Poisson- Gaussian noise. A variance stabilization transform is first I. INTRODUCTION applied to the image-data to remove the dependence be- tween the mean and variance of intensity values. This pre- LUORESCENCE video-microscopy is an investiga- processing requires the knowledge of parameters related F tion tool used in biology for dynamics analysis at to the acquisition system, also estimated in our approach. sub-cellular levels. Combined with fluorescent tags such In a second step, we propose an original statistical patch- as genetically engineered fluorescent chimeric proteins based framework for noise reduction and preservation of (e.g. Green Fluorescence Protein GFP), both confocal space-time discontinuities. In our study, discontinuities are microscopy and wide-field microscopy allow 3D live related to small moving spots with high velocity observed protein imaging. Mainly used to analyze isolated cells, in fluorescence video-microscopy. The idea is to minimize confocal microscopy can also be used in vivo if com- an objective non-local energy functional involving spatio- bined with endomicroscopy. Unfortunately, when cell temporal image patches. The minimizer has a simple form and is defined as the weighted average of input data viability needs to be preserved and photo-bleaching taken in spatially-varying neighborhoods. The size of each avoided, light exposure time must be limited, resulting neighborhood is optimized to improve the performance of in low signal-to-noise ratios. the pointwise estimator. The performance of the algorithm While improving the signal-to-noise ratio, denoising (which requires no motion estimation) is then evaluated on may allow us to reduce exposure time and therefore to both synthetic and real image sequences using qualitative open new opportunities in live cell imaging. Moreover, and quantitative criteria. frame rates can be increased without increasing radiation Index Terms—Video-microscopy, fluorescence, image se- dose, which could be relevant to capture fast events at sub-cellular levels. Finally, if the point spread function Copyright (c) 2009 IEEE. Personal use of this material is permitted. of the objective is not affected by denoising, images However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs- may still be compatible with a deconvolution process. [email protected]. This allows to significantly increase the performances of J. Boulanger was with UMR-144 Institut Curie/CNRS, Cell and deconvolution algorithms for images with low signal-to- Tissue Image Facility PICT-IBISA 75000 Paris, France and is now noise ratios and as a consequence, the ability to detect with the Radon Institute for Computational and Applied Mathematics 4040 Linz, Austria. (phone:+43 732 2468 5241; fax:+43 732 2468 and track objects of interest. 5212; email: [email protected]) Currently, denoising is a widely studied but still open C. Kervrann is with INRA, MIA 78352 Jouy-and-Josas, France and problem in image processing. Many methods have been INRIA Rennes Bretagne Atlantique, VISTA 35042 Rennes, France. described in the literature, and a recent comprehensive P. Bouthemy is with INRIA Rennes Bretagne Atlantique, VISTA 35042 Rennes, France. review can be found in [1], [2]. Methods based on P. Elbau is with the Radon Institute for Computational and Applied the full knowledge of noise statistics are probably the Mathematics 4040 Linz, Austria most efficient. In fluorescence video-microscopy, it is J.-B. Sibarita was with UMR-144 Institut Curie/CNRS, Cell and Tissue Image Facility PICT-IBISA 75000 Paris, France and is now established that the low level of fluorescence is related with UMR5091 Université Bordeaux 2/CNRS Institut François Ma- to a limited number of photons that can be modeled gendie 33077 Bordeaux, France as a Poisson process. Besides, additive electronic noise J. Salamero is with UMR-144 Institut Curie/CNRS, Cell and Tissue is usually present even if a cooling system is used on Image Facility PICT-IBISA 75248 Paris, France and with UMR 144 CNRS Institut Curie, Molecular mechanisms of intracellular the detector. Therefore the resulting images are assumed transport, 75248 Paris, France to be contaminated by a combination of Poisson and 2 Gaussian noise. Several approaches have been introduced image for denoising with the non-local means filter, is to deal with such signal-dependent noise. In [3], the untractable in practice in 2D, and unrealistic for video authors proposed a maximum penalized likelihood es- sequences. As a consequence, a variant of this filter has timator for Poisson noise removal in very low count been recently proposed in [31] in which the authors situations. The problem is more challenging for Poisson- use a pre-classification of the pixels of the sequence Gaussian noise and another line of work consists in in order to speed up the denoising procedure. Another stabilizing the noise variance using ad-hoc transforms. improvement introduced in [32] consists in collecting The more common transform is the so-called Anscombe similar patches to build 3D arrays. A unitary transform transform [4] designed for Poisson noise. This transform and a hard-thresholding are then applied to remove noise. was further generalized to Poisson-Gaussian noise [5], In the meanwhile, a general modeling framework based with satisfying results if the number of counts is large on signal theory and machine learning has been proposed enough and more recently for "clipped" (under- and by Elad et al. for image and video sequence analysis. The over-exposure) raw-data [6]. In the case of very low authors assume that the image is sparsely represented count situations ( 1 photons in average), the more over an over-complete dictionary of atoms that are either sophisticated Fisz≤ transform allows one to better stabi- fixed (e.g. DCT) or learned from exemplar patches [33], lize Poisson noise [7], [8]. Finally, local estimation of [34]. The approximation problem is then equivalent to image-dependent noise statistics (assumed to be locally the minimization (using a K-SVD algorithm) of an Gaussian) has also been investigated, especially in the energy functional involving a data term and a penalty case of adaptive Wiener filtering [9]–[11]. term that encodes sparsity [35]. This method is able to Denoising temporal sequences is even more com- produce impressive image denoising results, including plex since there are currently no satisfactory methods on image sequences, but requires intensive minimization for processing fluorescence video-microscopy 3D im- procedures and the adjustment of several parameters. age sequences contaminated by Poisson-Gaussian noise. Unlike the previous patch-based approaches [27], [31], Most of them only restore each frame separately with- [34], [36], we present in this paper a space-time patch- out using the temporal redundancy of image series based adaptive statistical method for 3D+time video- [12], [13]. When temporal coherence is exploited, it microscopy image sequence restoration. As already men- is usually recommended to consider a motion estima- tioned, patch-based methods have been proposed for tion/compensation stage as proposed for video denoising denoising image sequences, but, to our knowledge, only [14]–[17] and, for instance, for low-dose fluoroscopy anisotropic diffusion and wavelet shrinkage have been image sequence filtering [11]. This is especially true applied to 2D+time fluorescence video-microscopy [23], for real-time imaging applications. Thus, Kuznetsov et [24]. The main features of the proposed method have al. recently proposed to use a temporal Kalman-Bucy already been presented in a discrete setting at the IEEE- filter to improve the quality of video-microscopy im- ISBI’08 conference [37]. In our approach, we propose ages [18]. The main difficulty in video-microscopy is first a variance stabilization step to be applied to the to estimate the motion of small and similar objects data in order to obtain independence between the mean moving with high velocity in the image sequence. To and the variance. Second, we consider spatio-temporal overcome this problem, sophisticated methods (see [1]) neighborhoods to restore series of 3D images as already but designed for still images have been adapted to videos. proposed for 2D image sequences in [36]. Our method Wavelet shrinkage [19], [20], Wiener filtering [21] or is based on the minimization of an energy functional PDE-based methods [22] are typical examples of such while exploiting image patches. The minimizer of this methods. Some of them have been successfully adapted energy functional established in a continuous setting has to video-microscopy [23], [24]. Recently, an extension of a simple form and corresponds to a weighted average of the non-local means filter [1] also related to the universal intensity values taken in spatially (and temporally) vary- denoising (DUDE) algorithm [25] and the entropy-based ing neighborhoods. The neighborhood size is adapted UINTA filter [26], has been proposed to process image on-line to improve the performance (in the sense of the sequences. It assumes that an image sequence contains L2 risk) of the pointwise estimator. No learning step repeated patterns [27]. Noise can then be reduced by or wavelet decomposition is required. Also, no motion averaging data associated to the more similar patches in estimation is involved as originally described in [36]. the image sequence. Finally, patch-based approaches are Finally, the designed algorithm comprises only a few now very popular in texture synthesis [28], inpainting parameters which are easily calibrated.
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