Integration in the Fourier domain for restoration of a function from its slope : comparison of four methods

Altres ajuts: European Community project CTB556-01-4175.In some measurement techniques the profile, f(x), of a function should be obtained from the data on measured slope f'(x) by integration. The slope is measured in a given set of points, and from these data we should obtain the profile with the highest possible accuracy. Most frequently, the integration is carried out by numerical integration methods [Press et al., Numerical Recipes: The Art of Scientific Computing (Cambridge U. Press, Cambridge, 1987)] that assume different kinds of polynomial approximation of data between sampling points. We propose the integration of the function in the Fourier domain, by which the most-accurate interpolation is automatically carried out. Analysis of the integration methods in the Fourier domain permits us to easily study and compare the methods' behavior

Real Time Turbulent Video Perfecting by Image Stabilization and Super-Resolution

Image and video quality in Long Range Observation Systems (LOROS) suffer from atmospheric turbulence that causes small neighbourhoods in image frames to chaotically move in different directions and substantially hampers visual analysis of such image and video sequences. The paper presents a real-time algorithm for perfecting turbulence degraded videos by means of stabilization and resolution enhancement. The latter is achieved by exploiting the turbulent motion. The algorithm involves generation of a reference frame and estimation, for each incoming video frame, of a local image displacement map with respect to the reference frame; segmentation of the displacement map into two classes: stationary and moving objects and resolution enhancement of stationary objects, while preserving real motion. Experiments with synthetic and real-life sequences have shown that the enhanced videos, generated in real time, exhibit substantially better resolution and complete stabilization for stationary objects while retaining real motion.Comment: Submitted to The Seventh IASTED International Conference on Visualization, Imaging, and Image Processing (VIIP 2007) August, 2007 Palma de Mallorca, Spai

Optics-less smart sensors and a possible mechanism of cutaneous vision in nature

Optics-less cutaneous (skin) vision is not rare among living organisms, though its mechanisms and capabilities have not been thoroughly investigated. This paper demonstrates, using methods from statistical parameter estimation theory and numerical simulations, that an array of bare sensors with a natural cosine-law angular sensitivity arranged on a flat or curved surface has the ability to perform imaging tasks without any optics at all. The working principle of this type of optics-less sensor and the model developed here for determining sensor performance may be used to shed light upon possible mechanisms and capabilities of cutaneous vision in nature

Model-based object recognition from a complex binary imagery using genetic algorithm

This paper describes a technique for model-based object recognition in a noisy and cluttered environment, by extending the work presented in an earlier study by the authors. In order to accurately model small irregularly shaped objects, the model and the image are represented by their binary edge maps, rather then approximating them with straight line segments. The problem is then formulated as that of finding the best describing match between a hypothesized object and the image. A special form of template matching is used to deal with the noisy environment, where the templates are generated on-line by a Genetic Algorithm. For experiments, two complex test images have been considered and the results when compared with standard techniques indicate the scope for further research in this direction

Space-Variant Gabor Decomposition for Filtering 3D Medical Images

This is an experimental paper in which we introduce the possibility to analyze and to synthesize 3D medical images by using multivariate Gabor frames with Gaussian windows. Our purpose is to apply a space-variant filter-like operation in the space-frequency domain to correct medical images corrupted by different types of acquisitions errors. The Gabor frames are constructed with Gaussian windows sampled on non-separable lattices for a better packing of the space-frequency plane. An implementable solution for 3D-Gabor frames with non-separable lattice is given and numerical tests on simulated data are presented.Austrian Science Fund (FWF) P2751

Monte Carlo study of skin optical clearing to enhance light penetration in the tissue: implications for photodynamic therapy of acne vulgaris

ABSTRACT Result of Monte Carlo simulations of skin optical clearing is presented. The model calculations were carried out with the aim of studying of spectral response of skin under immersion liquids action and calculation of enhancement of light penetration depth. In summary, we have shown that: 1) application of glucose, propylene glycol and glycerol produced significant decrease of light scattering in different skin layers; 2) maximal clearing effect will be obtained in case of optical clearing of skin dermis, however, absorbed light fraction in skin dermis changed insignificantly, independently on clearing agent and place it administration; 3) in contrast to it, the light absorbed fraction in skin adipose layer increased significantly in case of optical clearing of skin dermis. It is very important because it can be used for development of optical methods of obesity treatment; 4) optical clearing of superficial skin layers can be used for decreasing of power of light radiation used for treatment of acne vulgaris

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.