PDE Based Enhancement of Color Images in RGB Space

International audienceA novel method for color image enhancement is proposed as an extension of scalar diffusion-shock filter coupling model, where noisy and blurred images are denoised and sharpened. The proposed model is based on using single vectors of the gradient magnitude and the second derivatives as a technique to relate different color components of the image. This model can be viewed as a generalization of Bettahar-Stambouli filter to multi-valued images. The proposed algorithm is more efficient than the mentioned filter and some previous works on color image denoising and deblurring without creating false colors

Метод повышения контраста медицинских видеоизображений с адаптивной глубиной коррекции для систем поддержки врачебных решений

Introduction. When conducting diagnostic examination of patients, various technological means are used to identify pathological conditions timely and accurately. The rapid development of sensors and imaging devices, as well as the advancement of modern diagnostic methods, facilitate the transition from the visual examination of images performed by a medical specialist towards the widespread use of automated diagnostic systems referred to as clinical decision support systems.Aim. To develop a method for enhancing the contrast of endoscopic images taking into account their features with the purpose of increasing the efficiency of medical diagnostic systems.Materials and methods. Contrast enhancement inevitably leads to an increase in the noise level. Despite the large number of different methods for noise reduction, their use at the preliminary stage of correction leads to the loss of small but important details. The development of a method for enhancing the contrast of endoscopic images was based on a nonlinear transformation of the intensity of pixels, taking into account their local neighborhood. Regression analysis was used to obtain a functional dependence between the depth of contrast correction and the degree of detail of the processed pixel neighborhood.Results. The results of experimental evaluation and comparison with conventional methods show that, under a comparable level of contrast enhancement, the proposed method provides a greater value of the structural similarity index towards to the original image (0.71 versus 0.63), with the noise level reduced by 17 %.Conclusion. In comparison with conventional methods, the developed method provides a simultaneous contrast correction of both light and dark image fragments and limits the growth of the noise level (typical of similar methods) by adapting the correction depth to the neighborhood features of the processed image element.Введение. При проведении диагностического осмотра или лечения врачу требуется быстро и точно выявлять и локализовывать аномалии и заболевания, для чего используются, в том числе, и технические средства. Быстрое развитие технологий в области датчиков, устройств визуализации и методов диагностики обеспечивает планомерный переход от анализа изображений врачом к широкому использованию автоматизированных диагностических систем – систем поддержки принятия врачебных решений.Цель работы. Разработка метода повышения контраста эндоскопических изображений с учетом их особенностей с целью увеличения эффективности медицинских диагностических систем.Материалы и методы. Повышение контраста неизбежно приводит к росту уровня шумов. Применение на предварительном этапе коррекции известных методов шумоподавления влечет за собой, как правило, потерю мелких деталей, которые важно сохранить при обработке. Разработан метод повышения контраста эндоскопических изображений, в основе которого лежит нелинейное преобразование яркости пикселов, учитывающее их локальную окрестность. Функциональная зависимость между глубиной коррекции контраста и оценкой детальности окрестности обрабатываемого пиксела получена с помощью регрессионного анализа.Результаты. Результаты экспериментальной оценки и сравнение с аналогом показывают, что при сопоставимом уровне повышения контраста обеспечено большее значение индекса структурного сходства с исходным изображением (0.71 против 0.63 у аналога) при уменьшении роста уровня шумов на 17 %.Заключение. Метод обеспечивает коррекцию контраста одновременно как светлых, так и темных фрагментов изображения и ограничивает при этом рост шумовой составляющей (характерный для методов этого класса) по сравнению со стандартными методами посредством адаптации глубины коррекции к свойствам окрестности обрабатываемого элемента изображения

Contents lists available at ScienceDirect Pattern Recognition

journal homepage: www.elsevier.com/locate/pr Edge-preserving smoothing using a similarity measure in adaptive geodesi

Multispectral texture synthesis

Synthesizing texture involves the ordering of pixels in a 2D arrangement so as to display certain known spatial correlations, generally as described by a sample texture. In an abstract sense, these pixels could be gray-scale values, RGB color values, or entire spectral curves. The focus of this work is to develop a practical synthesis framework that maintains this abstract view while synthesizing texture with high spectral dimension, effectively achieving spectral invariance. The principle idea is to use a single monochrome texture synthesis step to capture the spatial information in a multispectral texture. The first step is to use a global color space transform to condense the spatial information in a sample texture into a principle luminance channel. Then, a monochrome texture synthesis step generates the corresponding principle band in the synthetic texture. This spatial information is then used to condition the generation of spectral information. A number of variants of this general approach are introduced. The first uses a multiresolution transform to decompose the spatial information in the principle band into an equivalent scale/space representation. This information is encapsulated into a set of low order statistical constraints that are used to iteratively coerce white noise into the desired texture. The residual spectral information is then generated using a non-parametric Markov Ran dom field model (MRF). The remaining variants use a non-parametric MRF to generate the spatial and spectral components simultaneously. In this ap proach, multispectral texture is grown from a seed region by sampling from the set of nearest neighbors in the sample texture as identified by a template matching procedure in the principle band. The effectiveness of both algorithms is demonstrated on a number of texture examples ranging from greyscale to RGB textures, as well as 16, 22, 32 and 63 band spectral images. In addition to the standard visual test that predominates the literature, effort is made to quantify the accuracy of the synthesis using informative and effective metrics. These include first and second order statistical comparisons as well as statistical divergence tests

Error estimates for the Ginzburg-Landau approximation

Modulation equations play an essential role in the understanding of complicated dynamical systems near the threshold of instability. Here we look at systems defined over domains with one unbounded direction and show that the Ginzburg-Landau equation dominates the dynamics of the full problem, locally, at least over a long time-scale. As an application of our approximation theorem we look here at Bénard's problem. The method we use involves a careful handling of critical modes in the Fourier-transformed problem and an estimate of Gronwall's type

Spectral collocation methods

This review covers the theory and application of spectral collocation methods. Section 1 describes the fundamentals, and summarizes results pertaining to spectral approximations of functions. Some stability and convergence results are presented for simple elliptic, parabolic, and hyperbolic equations. Applications of these methods to fluid dynamics problems are discussed in Section 2

Semiannual final report, 1 October 1991 - 31 March 1992

A summary of research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, numerical analysis, and computer science during the period 1 Oct. 1991 through 31 Mar. 1992 is presented