Recursive conditional means image denoising

Methods and composition for denoising digital camera images are provided herein. The method is based on directly measuring the local statistical structure of natural images in a large training set that has been corrupted with noise mimicking digital camera noise. The measured statistics are conditional means of the ground truth pixel value given a local context of input pixels. Each conditional mean is the Bayes optimal (minimum mean squared error) estimate given the specific local context. The conditional means are measured and applied recursively (e.g., the second conditional mean is measured after denoising with the first conditional mean). Each local context vector consists of only three variables, and hence the conditional means can be measured directly without prior assumptions about the underlying probability distributions, and they can be stored in fixed lookup tables.Board of Regents, University of Texas Syste

Focus error estimation in images

Estimating focus error in an image involves a training phase and an application phase. In the training phase, an optical system is represented by a point-spread function. An image sensor array is represented by one or more wavelength sensitivity functions, one or more noise functions, and one or more spatial sampling functions. The point-spread function is applied to image patches for each of multiple defocus levels within a specified range to produce training data. Each of the images for each defocus level (i.e. focus error) is sampled using the wavelength sensitivity and spatial sampling functions. Noise is added using the noise functions. The responses from the sensor array to the training data are used to generate defocus filters for estimating focus error within the specified range. The defocus filters are then applied to the image patches of the training data and joint probability distributions of filter responses to each defocus level are characterized. In the application phase, the filter responses to arbitrary image patches are obtained and combined to derive continuous, signed estimates of the focus error of each arbitrary image patch.Board of Regents, University of Texas Syste

A method to integrate and classify normal distributions

Univariate and multivariate normal probability distributions are widely used when modeling decisions under uncertainty. Computing the performance of such models requires integrating these distributions over specific domains, which can vary widely across models. Besides some special cases where these integrals are easy to calculate, there exist no general analytical expressions, standard numerical methods or software for these integrals. Here we present mathematical results and open-source software that provide (i) the probability in any domain of a normal in any dimensions with any parameters, (ii) the probability density, cumulative distribution, and inverse cumulative distribution of any function of a normal vector, (iii) the classification errors among any number of normal distributions, the Bayes-optimal discriminability index and relation to the operating characteristic, (iv) dimension reduction and visualizations for such problems, and (v) tests for how reliably these methods may be used on given data. We demonstrate these tools with vision research applications of detecting occluding objects in natural scenes, and detecting camouflage.Comment: 14 pages, 8 figure

Natural systems analysis

ABSTRACT The environments we live in and the tasks we perform in those environments have shaped the design of our visual systems through evolution and experience. This is an obvious statement, but it implies three fundamental components of research we must have if we are going to gain a deep understanding of biological vision systems: (a) a rigorous science devoted to understanding natural environments and tasks, (b) mathematical and computational analysis of how to use such knowledge of the environment to perform natural tasks, and (c) experiments that allow rigorous measurement of behavioral and neural responses, either in natural tasks or in artificial tasks that capture the essence of natural tasks. This approach is illustrated with two example studies that combine measurements of natural scene statistics, derivation of Bayesian ideal observers that exploit those statistics, and psychophysical experiments that compare human and ideal performance in naturalistic tasks

Visual search under scotopic lighting conditions

AbstractWhen we search for visual targets in a cluttered background we systematically move our eyes around to bring different regions of the scene into foveal view. We explored how visual search behavior changes when the fovea is not functional, as is the case in scotopic vision. Scotopic contrast sensitivity is significantly lower overall, with a functional scotoma in the fovea. We found that in scotopic search, for a medium- and a low-spatial-frequency target, individuals made longer lasting fixations that were not broadly distributed across the entire search display but tended to peak in the upper center, especially for the medium-frequency target. The distributions of fixation locations are qualitatively similar to those of an ideal searcher that has human scotopic detectability across the visual field, and interestingly, these predicted distributions are different from those predicted by an ideal searcher with human photopic detectability. We conclude that although there are some qualitative differences between human and ideal search behavior, humans make principled adjustments in their search behavior as ambient light level decreases

Monorail/Foxa2 regulates floorplate differentiation and specification of oligodendrocytes, serotonergic raphe neurones and cranial motoneurones

In this study, we elucidate the roles of the winged-helix transcription factor Foxa2 in ventral CNS development in zebrafish. Through cloning of monorail (mol), which we find encodes the transcription factor Foxa2, and phenotypic analysis of mol(-/-) embryos, we show that floorplate is induced in the absence of Foxa2 function but fails to further differentiate. In mol(-/-) mutants, expression of Foxa and Hh family genes is not maintained in floorplate cells and lateral expansion of the floorplate fails to occur. Our results suggest that this is due to defects both in the regulation of Hh activity in medial floorplate cells as well as cell-autonomous requirements for Foxa2 in the prospective laterally positioned floorplate cells themselves. Foxa2 is also required for induction and/or patterning of several distinct cell types in the ventral CNS. Serotonergic neurones of the raphe nucleus and the trochlear motor nucleus are absent in mol(-/-) embryos, and oculomotor and facial motoneurones ectopically occupy ventral CNS midline positions in the midbrain and hindbrain. There is also a severe reduction of prospective oligodendrocytes in the midbrain and hindbrain. Finally, in the absence of Foxa2, at least two likely Hh pathway target genes are ectopically expressed in more dorsal regions of the midbrain and hindbrain ventricular neuroepithelium, raising the possibility that Foxa2 activity may normally be required to limit the range of action of secreted Hh proteins

Lower Levels of Cervicovaginal Tryptophan are Associated with Natural Clearance of Chlamydia in Women

Chlamydiatrachomatis (Ct) infection causes significant morbidity. In vitro studies demonstrate that Ct growth inhibition occurs by interferon-gamma (IFN-γ)–mediated depletion of intracellular tryptophan, and some Ct strains utilize extracellular indole to restore tryptophan levels. Whether tryptophan levels are associated with Ct infection clearance in humans remains unknown. We evaluated tryptophan, indole, and IFN-γ levels in cervicovaginal lavages from women with either naturally cleared or persisting Ct infection. Women who cleared infection had significantly lower tryptophan levels and trended toward lower IFN-γ levels compared to women with persisting infection. Due to its volatility, indole was not measurable in either group

Human Female Genital Tract Infection by the Obligate Intracellular Bacterium Chlamydia trachomatis Elicits Robust Type 2 Immunity

While Chlamydia trachomatis infections are frequently asymptomatic, mechanisms that regulate host response to this intracellular Gram-negative bacterium remain undefined. This investigation thus used peripheral blood mononuclear cells and endometrial tissue from women with or without Chlamydia genital tract infection to better define this response. Initial genome-wide microarray analysis revealed highly elevated expression of matrix metalloproteinase 10 and other molecules characteristic of Type 2 immunity (e.g., fibrosis and wound repair) in Chlamydia-infected tissue. This result was corroborated in flow cytometry and immunohistochemistry studies that showed extant upper genital tract Chlamydia infection was associated with increased co-expression of CD200 receptor and CD206 (markers of alternative macrophage activation) by endometrial macrophages as well as increased expression of GATA-3 (the transcription factor regulating TH2 differentiation) by endometrial CD4+ T cells. Also among women with genital tract Chlamydia infection, peripheral CD3+ CD4+ and CD3+ CD4- cells that proliferated in response to ex vivo stimulation with inactivated chlamydial antigen secreted significantly more interleukin (IL)-4 than tumor necrosis factor, interferon-γ, or IL-17; findings that repeated in T cells isolated from these same women 1 and 4 months after infection had been eradicated. Our results thus newly reveal that genital infection by an obligate intracellular bacterium induces polarization towards Type 2 immunity, including Chlamydia-specific TH2 development. Based on these findings, we now speculate that Type 2 immunity was selected by evolution as the host response to C. trachomatis in the human female genital tract to control infection and minimize immunopathological damage to vital reproductive structures. © 2013 Vicetti Miguel et al

Human Wavelength Discrimination of Monochromatic Light Explained by Optimal Wavelength Decoding of Light of Unknown Intensity

We show that human ability to discriminate the wavelength of monochromatic light can be understood as maximum likelihood decoding of the cone absorptions, with a signal processing efficiency that is independent of the wavelength. This work is built on the framework of ideal observer analysis of visual discrimination used in many previous works. A distinctive aspect of our work is that we highlight a perceptual confound that observers should confuse a change in input light wavelength with a change in input intensity. Hence a simple ideal observer model which assumes that an observer has a full knowledge of input intensity should over-estimate human ability in discriminating wavelengths of two inputs of unequal intensity. This confound also makes it difficult to consistently measure human ability in wavelength discrimination by asking observers to distinguish two input colors while matching their brightness. We argue that the best experimental method for reliable measurement of discrimination thresholds is the one of Pokorny and Smith, in which observers only need to distinguish two inputs, regardless of whether they differ in hue or brightness. We mathematically formulate wavelength discrimination under this wavelength-intensity confound and show a good agreement between our theoretical prediction and the behavioral data. Our analysis explains why the discrimination threshold varies with the input wavelength, and shows how sensitively the threshold depends on the relative densities of the three types of cones in the retina (and in particular predict discriminations in dichromats). Our mathematical formulation and solution can be applied to general problems of sensory discrimination when there is a perceptual confound from other sensory feature dimensions