Edges, Transitions and Criticality

International audienceIn this article, various notions of edges encountered in digital image process- ing are reviewed in terms of compact representation (or completion). We show that critical exponents defined in Statistical Physics lead to a much more coherent definition of edges, consistent across the scales in acquisitions of natural phenomena, such as high resolution natural images or turbulent acquisitions. Edges belong to the multiscale hierarchy of an underlying dy- namics, they are understood from a statistical perspective well adapted to fit the case of natural images. Numerical computation methods for the eval- uation of critical exponents in the non-ergodic case are recalled, which apply for the vast majority of natural images. We study the framework of re- constructible systems in a microcanonical formulation, show how it redefines edge completion, and how it can be used to evaluate and assess quantitatively the adequation of edges as candidates for compact representations. We study with particular attention the case of turbulent data, in which edges in the classical sense are particularly challenged. Tests are conducted and evalu- ated on a standard database for natural images. We test the newly intro- duced compact representation as an ideal candidate for evaluating turbulent cascading properties of complex images, and we show better reconstruction performance than the classical tested methods

Bayesian Approach in a Learning-Based Hyperspectral Image Denoising Framework

International audienceHyperspectral images are corrupted by a combination of Gaussian-impulse noise. On one hand, the traditional approach of handling the denoising problem using maximum a posteriori criterion is often restricted by the time-consuming iterative optimization process and design of hand-crafted priors to obtain an optimal result. On the other hand, the discriminative learning-based approaches offer fast inference speed over a trained model; but are highly sensitive to the noise level used for training. A discriminative model trained with a loss function which does not accord with the Bayesian degradation process often leads to sub-optimal results. In this paper, we design the training paradigm emphasizing the role of loss functions; similar to as observed in model-based optimization methods. As a result; loss functions derived in Bayesian setting and employed in neural network training boosts the denoising performance. Extensive analysis and experimental results on synthetically corrupted and real hyperspectral dataset suggest the potential applicability of the proposed technique under a wide range of homogeneous and heterogeneous noisy settings. INDEX TERMS Bayesian estimation, discriminative learning, Gaussian-impulse noise, hyperspectral imaging, residual network

A Multifractal-based Wavefront Phase Estimation Technique for Ground-based Astronomical Observations

International audienceTurbulence in the Earth's atmosphere interferes with the propagation of planar wavefronts from outer space resulting in a phase distorted non-planar wavefront. This phase distortion is responsible for the refractive blurring of images accounting to the loss in spatial resolution power of ground-based telescopes. The technology widely used to remove this phase distortion is Adaptive Optics (AO). In AO, an estimate of the distorted phase is provided by a wavefront sensor (WFS) in the form of low-resolution slope measurements of the wavefront. The estimate is then used to create a corrected wavefront, that (approximately) removes the phase distortion from the incoming wavefronts. Phase reconstruction from WFS measurements is done by solving large linear systems followed by interpolating the low-resolution phase to its desired high-resolution. In this paper, we propose an alternate technique to wavefront phase reconstruction using concepts derived from the Microcanonical Multiscale Formalism (MMF), which is a specific approach to multifractality. We take into account an a priori information of the wavefront phase, provided by the multifractal exponents. Then through the framework of multiresolution analysis and wavelet transform, we address the problem of phase reconstruction from low-resolution WFS measurements. Comparison, in terms of reconstruction quality, with classical techniques in AO proves the superiority of our approach

Reconstructing an image from its edge representation

International audienceIn this paper, we show that a new edge detection scheme developed from the notion of transition in nonlinear physics, associated with the precise computation of its quantitative parameters (most notably singularity exponents) provide enhanced performances in terms of reconstruction of the whole image from its edge representation; moreover it is naturally robust to noise. The study of biological vision in mammals state the fact that major information in an image is encoded in its edges, the idea further supported by neurophysics. The first conclusion that can be drawn from this stated fact is that of being able to reconstruct accurately an image from the compact representation of its edge pixels. The paper focuses on how the idea of edge completion can be assessed quantitatively from the framework of reconstructible systems when evaluated in a microcanonical formulation; and how it redefines the adequation of edge as candidates for compact representation. In the process of doing so, we also propose an algorithm for image reconstruction from its edge feature and show that this new algorithm outperforms the well-known 'state-of-the-art' techniques, in terms of compact representation, in majority of the cases

Structure-Preserving Denoising of SAR Images Using Multifractal Feature Analysis

International audienceIn this letter, we propose a speckle removal denois-ing algorithm for synthetic aperture radar (SAR) images. The approach is based on the concept of extracting informative feature (based on the concept of multifractal decomposition of signals) from a speckle-induced SAR image and then estimating a noise-free image from the gradients restricted to those features. The experimental results show that the proposed technique not only improves the visual quality of the SAR images but also effectively preserves their texture. Comparison with the classical and state-of-the-art denoising techniques shows the advantages of the proposed scheme, both visually and quantitatively

Screening for hemoglobinopathies in a socially disadvantaged population from a rural district of West Bengal, India

Background: Detection of traits/carriers plays an important role in preventing the birth of a thalassemic child. West Bengal, one of the eastern states in India is the home to a bulk of socially challenged population including scheduled castes and scheduled tribes among others. The present study aimed to detect the prevalence of different hemoglobinopathies in a socially challenged district of West Bengal.Methods: In this retrospective cross sectional study thalassemia detection camps were organized at the community level over a period of four years. Venous blood samples were subjected to complete hemogram and high performance liquid chromatography (HPLC). In few difficult cases samples were sent to the reference laboratory for molecular characterization. The prevalence of heterozygous, homozygous or compound heterozygous states of different thalassemias and hemoglobinopathies across various respondent groups (e.g. children, premarital, postmarital and antenatal) and existing caste categories (scheduled tribes, scheduled caste and general) were analyzed.Results: We analyzed a total of 114,606 HPLC reports; 18681 (16.30%), 15438 (13.47%) and 80487 (70.23%) cases belonged to scheduled tribes, scheduled castes and general category respectively. Out of 114,606 cases, 11,001 (9.6%) had revealed abnormal hemoglobins; beta thalassaemia trait was the most common (6.63%; n=7602) across all subgroup analysis. Among others, HbE trait, sickle cell trait and HbD trait were detected in 1788 (1.56%), 1362 (1.18%) and 126 (0.11%) cases respectively.Conclusions: Beta thalassaemia trait and HbE trait are the common haemoglobin variants in this rural district of West Bengal. The prevalence of sickle gene revealed in the present study is much less than previous studies in the locality

Antimicrobial resistance pattern, clustering mechanisms and correlation matrix of drug-resistant Escherichia coli in black Bengal goats in West Bengal, India

A cross-sectional study covering four agro-climatic zones of West Bengal, India, was carried out to understand the risk-factors, antimicrobial resistance mechanism and clustering of the resistance characteristics of Escherichia coli isolated from healthy (170) and diarrhoeic (74) goats reared under intensive (52) and semi-intensive (192) farming practices. Of the 488 E. coli isolates, the majority, including the extended spectrum (n: 64, 13.11%) and AmpC β-lactamase (ACBL) (n: 86, 17.62%) producers, were resistant to tetracycline (25.2%), followed by enrofloxacin (24.5%), cefotaxime (21.5%) and amikacin (20.5%). Statistical modelling revealed that the isolates from diarrhoeic animals (p < 0.001) are likely to be more ACBL-positive than those from the healthy counterparts. Similarly, cefotaxime (p < 0.05) and enrofloxacin-resistance (p < 0.01) were significantly higher in diarrhoeic goats and in goats reared intensively. The isolates (n = 35) resistant to multiple drugs revealed the presence of β-lactamase [blaCTXM-1-(21), blaSHV-(7), blaTEM-(3), blaCMY-6-(1), blaCITM-(3)]; quinolone [qnrB-(10), qnrS-(7), aac(6’)-Ib-cr-(3)]; tetracycline [tetA-(19), tetB-(4)] and sulphonamide resistance determinants [sul1-(4)]; multiple plasmids, especially those belonging to the IncF and IncI1 replicon types; and active acrAB efflux pumps. Further, two isolates harbored the carbapenem resistance (blaNDM-5) gene and eight were strong biofilm producers. This first ever study conducted to unravel the status of AMR in goat farming reveals that not only the intensive farming practices but also certain clinical ailments such as diarrhoea can increase the shedding of the drug-resistant isolate. The emergence of multi-drug resistant (MDR) E. coli in goats, particularly those that are carbapenem resistant, is a cause for concern that indicates the spread of such pathogens even in the livestock sub-sector generally considered as naive

Experimental assessment of arsenic toxicity in garole sheep in India

Arsenic, a dangerous bio-accumulative poison, is a grave threat affecting a large number of people as well as animals throughout the World, particularly in Bangladesh and West Bengal, India. It is also a matter of concern as continuously entering into food chain through biotic and abiotic products. The present study was conducted to evaluate the experimental effect of arsenic toxicosis on Garole sheep of West Bengal. One group was subjected to oral arsenic exposure @ 6.6 mg Kg−1 over 133 days when rests considered as negative control. Periodical arsenic estimation in wool, urine and feces along with hemato-biochemical alteration were checked thoroughly. It was evident from the study that long term arsenic exposure exerted a significant (p < 0.01) alteration compared to normal animal which were further supported by clinical abnormalities. Exposed animals showed histological changes throughout major internal organs like coagulative necrosis of liver, tubular nephritis of kidney and acanthosis of skin etc. The bio-accumulative and excretion pattern of arsenic inside body were also well understood by the arsenic estimation study of wool, urine and feces which may be helpful for discussion regarding arsenic entry into food chain via animals