Novel effective fuzzy diffusion algorithm for noise removal

Transmittance and reflectance from spruce wood and bovine ligamentum nuchae as two different fibrous media are examined by time-of-flight spectroscopy for varying source detector separations and several orientations of the fibers in the sample. The anisotropic diffusion theory is used to obtain the absorption coefficient and the diffusion coefficients parallel and perpendicular to the fibers. The results are compared to those obtained with the isotropic diffusion theory. It is shown that for increasing source detector separations, the retrieved optical properties change as expected from Monte Carlo simulations performed in a previous study. This confirms that the anisotropic diffusion theory yields useful results for certain experimental conditions.

Pulse coupled neural network based anisotropic diffusion method for 1/f noise reduction

Deceptive path planning (DPP) aims to find a path that minimizes the probability of the observer identifying the real goal of the observed before it reaches. It is important for addressing issues such as public safety, strategic path planning, and logistics route privacy protection. Existing traditional methods often rely on “dissimulation”—hiding the truth—to obscure paths while ignoring the time constraints. Building upon the theory of probabilistic goal recognition based on cost difference, we proposed a DPP method, DPP_Q, based on count-based Q-learning for solving the DPP problems in discrete path-planning domains under specific time constraints. Furthermore, to extend this method to continuous domains, we proposed a new model of probabilistic goal recognition called the Approximate Goal Recognition Model (AGRM) and verified its feasibility in discrete path-planning domains. Finally, we also proposed a DPP method based on proximal policy optimization for continuous path-planning domains under specific time constraints called DPP_PPO. DPP methods like DPP_Q and DPP_PPO are types of research that have not yet been explored in the field of path planning. Experimental results show that, in discrete domains, compared to traditional methods, DPP_Q exhibits better effectiveness in enhancing the average deceptiveness of paths. (Improved on average by 12.53% compared to traditional methods). In continuous domains, DPP_PPO shows significant advantages over random walk methods. Both DPP_Q and DPP_PPO demonstrate good applicability in path-planning domains with uncomplicated obstacles.

Speckle noise removal plays a crucial role in ultrasound (US) image diagnosis, since the visual quality of the US images are largely corrupted by speckle noise. Numerous speckle noise removal techniques have been proposed in the literature based on anisotropic filtering, wavelets and morphology; however they have some major problems like loss of edge information, texture information and inability to remove low frequency noise. Despeckling of US images is usually carried out using conventional anisotropic diffusion or speckle reducing anisotropic diffusion. However, despeckling US images may not be able to preserve the edges which comprises of important clinical information. To overcome the issues in speckle noise removal (despeckling) of US images, contourlet transform based anisotropic nonlinear diffusion filtering is proposed in this paper. Contourlet transform improves some important features like multiscale and directionality. Adaptive nonlinear diffusion has been incorporated in anisotropic filtering to improve the filtering performance. The comparison performance of the proposed method with other despeckling techniques indicates that it has better noise removal performance for medical US images.

Extended-dimensional anisotropic diffusion using diffusion paths on inter-color planes for noise reduction

An improved anisotropic diffusion filter with semi-adaptive threshold for edge preservation

In previous works, it has been demonstrated that for filtered backprojection (FBP) reconstruction-based computed tomography (CT) images, the measured CT numbers are biased and the bias level decreases with increasing radiation dose. Low-dose scans typically include noise reduction schemes to reduce noise level. The purpose of this work was to investigate the potential impact of different noise reduction schemes on the CT number bias. Three different filtration methods: Gaussian, adaptive trimmed mean (ATM), and anisotropic diffusion (AD) were implemented to reduce noise. All filters were independently applied in three different domains: raw counts, log-processed sinogram, or reconstructed image domain. A quality assurance phantom was scanned on a benchtop CT cone beam CT system, at dose levels ranging from 0.6 to 4.0mGy. The conventional FBP reconstructions were performed to reconstruct CT images for the study of CT number biases. The CT number bias of different material inserts in the phantom was then measured. To further study the overall impact of CT number bias together with the potential consequences of noise reduction schemes on both the spatial resolution and noise characteristics, the task-based detectability of a high-contrast and high spatial resolution imaging task was used as an example to assess the performance of each noise reduction scheme. To qualitatively assess the impact of these noise reduction schemes on image, an anthropomorphic head phantom was alsoscanned on the benchtop CT system and processed with the above noise reduction schemes to generate images for demonstration. Our results demonstrated the following major findings: (a) CT number bias can be significantly reduced when the noise reduction schemes are implemented in the raw counts domain; CT number bias cannot be reduced when these noise reduction schemes are implemented either in the reconstructed image domain or in the log-processed sinogram domain. (b) The extent of CT number bias reduction is dependent on both the material composition and noise reduction parameters. (c) The overall impact of the noise reduction schemes can be studied using the task-based detectability analysis framework and this framework can be used to select the appropriate parameters in each noise reduction scheme to optimize the performance for a given imaging task. Noise reduction schemes can be used to considerably reduce CT number bias when they are implemented in the raw counts domain; however, their application cannot be arbitrarily extended to either the log-processed sinogram data domain or image domain. Trade-offs between bias reduction and overall image quality must be studied for an optimal performance of a given imaging task.

다양한 조명 환경 하에서, 얼굴인식이 잘 동작하도록 하는 것은 매우 어려운 일이며 성공적인 상업화를 위해서는 반드시 성취되어야 하는 작업이다. 본 논문에서는 얼굴 인식을 위한 효율적인 조명 전처리 방법을 제안한다. Anisotropic smoothing 기반 조명 전처리 방법은 조명 전처리 방법 가운데 효과적인 방법으로 잘 알려져 있으나, 원 이미지의 명도 대비를 감소시키며 에지 성분의 약화를 초래한다. 본 논문의 제안 방법은 기존 anisotropic smoothing 방법을 개선하여, 조명의 영향을 줄이면서 명도 대비를 증가시키고 에지 정보를 강화한다. 이러한 개선의 결과로, 본 논문의 제안 방법에 의해 조명 전처리된 같은 사람의 얼굴 이미지들은 보다 차별적인 특징 벡터(가버 특징 벡터)를 갖게 된다. 본 논문에서 제안한 조명 전처리 방법의 효율성은 가버젯 유사도를 이용한 얼굴 인식의 실험을 통하여 입증되었다. Robust face recognition under various illumination environments is very difficult and needs to be accomplished for successful commercialization. In this paper, we propose an efficient illumination preprocessing method for face recognition. illumination preprocessing algorithm based on anisotropic smoothing is well known to be effective among illumination normalization methods but deteriorates the intensity contrast of the original image, and incurs less sharp edges. The proposed method in this paper improves the previous anisotropic smoothing based illumination normalization method so that it increases the intensity contrast and enhances the edges while diminishing effects of illumination. Due to the result of these improvements, face images preprocessed by the proposed illumination preprocessing method becomes to have more distinctive feature vectors(Gabor feature vectors). Through experiments of face recognition using Gabor jet similarity, the effectiveness of the proposed illumination preprocessing method is verified.

In plant vascular tissue development, different cell wall patterns are formed, offering different mechanical properties optimised for different growth stages. Critical in these patterning processes are Rho of Plants (ROP) proteins, a class of evolutionarily conserved small GTPase proteins responsible for local membrane domain formation in many organisms. While te spotted metaxylem pattern can easily be understood as a result of a Turing-style reaction-diffusion mechanism, it remains an open question how the consistent orientation of evenly spaced bands and spirals as found in protoxylem is achieved. We hypothesise that this orientation results from an interaction between ROPs and an array of transversely oriented cortical microtubules that acts as a directional diffusion barrier. Here, we explore this hypothesis using partial differential equation models with anisotropic ROP diffusion and show that a horizontal microtubule array acting as a vertical diffusion barrier to active ROP can yield a horizontally banded ROP pattern. We then study the underlying mechanism in more detail, finding that it can only orient curved pattern features but not straight lines. This implies that, once formed, banded and spiral patterns cannot be reoriented by this mechanism. Finally, we observe that ROPs and microtubules together only form ultimately static patterns if the interaction is implemented with sufficient biological realism.

Stochastic Satisfiability Modulo Theories (SSMT) [1] is a quantitative extension of classical Satisfiability Modulo Theories (SMT) inspired by stochastic logics. It extends SMT by the usual as well as randomized quantifiers, facilitating capture of stochastic game properties in the logic, like reachability analysis of hybrid-state Markov decision processes. Solving for SSMT formulae with quantification over finite and thus discrete domain has been addressed by Tino Teige et al. [2]. In this paper, we extend their work to SSMT over continuous quantifier domains (CSSMT) in order to enable capture of continuous disturbances and uncertainty in hybrid systems. We extend the semantics of SSMT and introduce a corresponding solving procedure. A simple case study is pursued to demonstrate applicability of our framework to reachability problems in hybrid systems.

We propose an innovative procedure for extracting decay regions from color images of stony materials. The use of appropriate image analysis techniques can offer an important contribution to be used together with the traditional methodologies for studying and diagnosing the decay of stony materials that constitute ancient monuments. The presented approach is constituted by the PDE (Partial Differential Equations) model of anisotropic diffusion and by the level set/fast marching method. The anisotropic diffusion is applied in order to limit the smoothing at the zones of high gradient. In the segmentation process, the numerical technique of the level set/fast marching is applied in order to extract from the image only the color region examined. The study case concerns impressive remains of the city of Aosta (Italy).

A nonlinear discontinuity-preserving method for noise removal for band pass signals such as signals modulated with Binary Phase-Shift Keying (BPSK) modulation is proposed in this paper. This method is inspired by the anisotropic diffusion algorithm to remove noise and preserve discontinuities in band pass signals modulated with a single frequency. It is demonstrated here that nonlinear noise removal method for a real valued band pass signal requires a solution for a nonlinear partial differential equation which is of fourth order in space and second order in time. The results presented in this work show better performance in nonlinear noise removal for real valued band pass signals in comparison with the previous work in the literature.

Discrete domain swarm robotics is an emerging and challenging field of research. Unlike continuous domain where working place of the robots is a two-dimensional plane, in the discrete domain their working place is modelled by a graph. Robots are deployed on the nodes of a given graph, and they are allowed to move only along the edges of that graph. Consequently, the models used in the continuous domain are not always applicable in the discrete domain. Exemplified by the gathering problem, this article critically reviews models, assumptions, and approaches that have been proposed in solving the problems in the discrete domain.

There is currently no universally accepted approach to weaning patients from mechanical ventilation, but there is clearly a feeling within the medical community that it may be possible to formulate the weaning process algorithmically in some manner. Fuzzy logic seems suited to this task because of the way it so naturally represents the subjective human notions employed in much of medical decision‐making. The purpose of the present study was to develop a fuzzy logic algorithm for controlling pressure support ventilation in patients in the intensive care unit, utilizing measurements of heart rate, tidal volume, breathing frequency and arterial oxygen saturation. In this report, we describe the fuzzy logic algorithm and demonstrate its use retrospectively in 13 patients with severe chronic obstructive pulmonary disease, by comparing the decisions made by the algorithm with what actually transpired. The fuzzy logic recommendation agreed with the status quo to within 2 cm H2O an average of 76% of the time, and to within 4 cm H2O an average of 88% of the time (although in most of these instances no medical decisions were taken as to whether or not to change the level of ventilatory support). We also compared the predictions of our algorithm with those cases in which changes in pressure support level were actually made by an attending physician, and found that the physicians tended to reduce the support level somewhat more aggressively than the algorithm did. We conclude that our fuzzy algorithm has the potential to control the level of pressure support ventilation from ongoing measurements of a patient’s vital signs. Abstract reprinted from the American Journal of Respiratory and Critical Care Medicine, volume 160, Nemoto T et al., ‘(1999) Automatic control of pressure support mechanical ventilation using fuzzy logic.’, pages 550–556. © 1999, reproduced with permission from The American Thoracic Society.

The aim of the present study was to investigate a new type of Bayesian reconstruction method which utilizes partial differential equations (PDE) image models as a prior. PDE image models are very popular in image restoration and segmentation. Our method introduces such models to emission tomography reconstruction by using Bayesian one step late (OSL) algorithm and an OS acceleration one. In a PDE based model, the image can be viewed as the solution of an evolutionary differential equation. It can be thought as a descent of an energy function which entitled us to use PDE models in Bayesian reconstruction. In this paper, three different PDE models are studied, two of them are based on anisotropic diffusion model, and another is based on a complex diffusion model. All of them have the properties of edge-preserving and denoising as the latest median root prior (MRP). We validated the effectiveness of the method using a Zubal phantom in numerical experiments and compared it to the classical MLEM and MRP reconstruction. The results show that the proposed PDE model method is better than the MLEM and the MRP reconstruction methods in visualization, bias and variance, and are more suitable for OS acceleration than MRP.