Nonlocal similarity image filtering

Abstract. We exploit the recurrence of structures at different locations, orientations and scales in an image to perform denoising. While previous methods based on “nonlocal filtering ” identify corresponding patches only up to translations, we consider more general similarity transformations. Due to the additional computational burden, we break the problem down into two steps: First, we extract similarity invariant descriptors at each pixel location; second, we search for similar patches by matching descriptors. The descriptors used are inspired by scale-invariant feature transform (SIFT), whereas the similarity search is solved via the minimization of a cost function adapted from local denoising methods. Our method compares favorably with existing denoising algorithms as tested on several datasets.

Synergistic degradation of diazo dye Direct Red 5B by Portulaca grandiflora and Pseudomonas putida

Plants and bacterial consortium of Portulaca grandiflora and Pseudomonas putida showed complete decolorization of a sulfonated diazo dye Direct Red 5B within 72 h, while in vitro cultures of P. grandiflora and P. putida independently showed 92 and 81 % decolorization within 96 h, respectively. A significant induction in the activities of lignin peroxidase, tyrosinase, 2,6-dichlorophenol indophenol reductase and riboflavin reductase was observed in the roots of P. grandiflora during dye decolorization; whereas, the activities of laccase, veratryl alcohol oxidase and 2,6-dichlorophenol indophenol reductase were induced in the cells of P. putida. Plant and bacterial enzymes in the consortium gave an enhanced decolorization of Direct Red 5B synergistically. The metabolites formed after dye degradation analyzed by UV-Vis spectroscopy, Fourier transformed infrared spectroscopy and high performance liquid chromatography confirmed the biotransformation of Direct Red 5B. Differential fate of metabolism of Direct Red 5B by P. grandiflora, P. putida and their consortium were proposed with the help of gas chromatography-mass spectroscopy analysis. P. grandiflora metabolized the dye to give 1-(4-diazenylphenyl)-2-phenyldiazene, 7-(benzylamino) naphthalene-2-sulfonic acid, 7-aminonaphthalene-2-sulfonic acid and methylbenzene. P. putida gave 4-hydroxybenzenesulfonic acid and 4-hydroxynaphthalene-2-sulfonic acid and benzamide. Consortium showed the formation of benzenesulfonic acid, 4-diazenylphenol, 6-aminonaphthalen-1-ol, methylbenzene and naphthalen-1-ol. Consortium achieved an enhanced and efficient degradation of Direct Red 5B. Phytotoxicity study revealed the nontoxic nature of metabolites formed after parent dye degradation. Use of such combinatorial systems of plant and bacteria could prove to be an effective and efficient strategy for the removal of textile dyes from soil and waterways

Surface smoothing: a way back in early brain morphogenesis.

International audienceIn this article we propose to investigate the analogy between early cortical folding process and cortical smoothing by mean curvature flow. First, we introduce a one-parameter model that is able to fit a developmental trajectory as represented in a Volume-Area plot and we propose an efficient optimization strategy for parameter estimation. Second, we validate the model on forty cortical surfaces of preterm newborns by comparing global geometrical indices and trajectories of central sulcus along developmental and simulation time

Double adaptive filtering of Gaussian noise degraded images

Good estimate and simulation of the behavior of additive noise is central to the adaptive restoration of images corrupted with Gaussian noise. This paper presents a double adaptive filtering scheme in the sense that the filter is able to estimate the variance of additive noise in order to determine the filter gain for pixel updating, and also able to decide if the pixel should remain unfiltered. Experimental results obtained from the restoration of several images have shown the superiority of the proposed method to some benchmark image filters

J.C.: A fuzzy, nonparametric segmentation framework for DTI and MRI analysis

Abstract. This paper presents a novel statistical fuzzy-segmentation method for diffusion tensor (DT) images and magnetic resonance (MR) images. Typical fuzzy-segmentation schemes, e.g. those based on fuzzy-C-means (FCM), incorporate Gaussian class models which are inherently biased towards ellipsoidal clusters. Fiber bundles in DT images, however, comprise tensors that can inherently lie on more-complex manifolds. Unlike FCM-based schemes, the proposed method relies on modeling the manifolds underlying the classes by incorporating nonparametric datadriven statistical models. It produces an optimal fuzzy segmentation by maximizing a novel information-theoretic energy in a Markov-randomfield framework. For DT images, the paper describes a consistent statistical technique for nonparametric modeling in Riemannian DT spaces that incorporates two very recent works. In this way, the proposed method provides uncertainties in the segmentation decisions, which stem from imaging artifacts including noise, partial voluming, and inhomogeneity. The paper shows results on synthetic and real, DT as well as MR images.

A variational framework for non-local image inpainting

Non-local methods for image denoising and inpainting have gained considerable attention in recent years. This is in part due to their superior performance in textured images and regions, a known weakness of purely local methods. Local methods on the other hand have demonstrated to be very appropriate for the recovering of geometric structure such as image edges. The synthesis of both types of methods is a trend in current research. Variational analysis in particular is an appropriate tool for a unified treatment of local and non-local methods. In this work we propose a general variational framework for the problem of non-local image inpainting, from which several previous inpainting schemes can be derived, in addition to leading to novel ones. We explicitly study some of these, relating them to previous work and showing results on synthetic and real images