Edge detection and noise removal by use of a partial differential equation with automatic selection of parameters

This work deals with noise removal by the use of an edge preserving method whose parameters are automatically estimated, for any application, by simply providing information about the standard deviation noise level we wish to eliminate. The desired noiseless image u(x), in a Partial Differential Equation based model, can be viewed as the solution of an evolutionary differential equation u t(x) = F(u xx, u x, u, x, t) which means that the true solution will be reached when t ® ¥. In practical applications we should stop the time ''t'' at some moment during this evolutionary process. This work presents a sufficient condition, related to time t and to the standard deviation s of the noise we desire to remove, which gives a constant T such that u(x, T) is a good approximation of u(x). The approach here focused on edge preservation during the noise elimination process as its main characteristic. The balance between edge points and interior points is carried out by a function g which depends on the initial noisy image u(x, t0), the standard deviation of the noise we want to eliminate and a constant k. The k parameter estimation is also presented in this work therefore making, the proposed model automatic. The model's feasibility and the choice of the optimal time scale is evident through out the various experimental results.

A Decomposition and Noise Removal Method Combining Diffusion Equation and Wave Atoms for Textured Images

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A well-balanced flow equation for noise removal and edge detection

In this paper, an anisotropic nonlinear diffusion equation for image restoration is presented. The model has two terms: the diffusion and the forcing term. The balance between these terms is made in a selective way, in which boundary points and interior points of the objects that make up the image are treated differently. The optimal smoothing time concept, which allows for finding the ideal stop time for the evolution of the partial differential equation is also proposed. Numerical results show the proposed model's high performance.

Generalized methods and solvers for noise removal from piecewise constant signals. I. Background theory

Removing noise from piecewise constant (PWC) signals is a challenging signal processing problem arising in many practical contexts. For example, in exploration geosciences, noisy drill hole records need to be separated into stratigraphic zones, and in biophysics, jumps between molecular dwell states have to be extracted from noisy fluorescence microscopy signals. Many PWC denoising methods exist, including total variation regularization, mean shift clustering, stepwise jump placement, running medians, convex clustering shrinkage and bilateral filtering; conventional linear signal processing methods are fundamentally unsuited. This paper (part I, the first of two) shows that most of these methods are associated with a special case of a generalized functional, minimized to achieve PWC denoising. The minimizer can be obtained by diverse solver algorithms, including stepwise jump placement, convex programming, finite differences, iterated running medians, least angle regression, regularization path following and coordinate descent. In the second paper, part II, we introduce novel PWC denoising methods, and comparisons between these methods performed on synthetic and real signals, showing that the new understanding of the problem gained in part I leads to new methods that have a useful role to play.

Generalized methods and solvers for noise removal from piecewise constant signals. II. New methods

Removing noise from signals which are piecewise constant (PWC) is a challenging signal processing problem that arises in many practical scientific and engineering contexts. In the first paper (part I) of this series of two, we presented background theory building on results from the image processing community to show that the majority of these algorithms, and more proposed in the wider literature, are each associated with a special case of a generalized functional, that, when minimized, solves the PWC denoising problem. It shows how the minimizer can be obtained by a range of computational solver algorithms. In this second paper (part II), using this understanding developed in part I, we introduce several novel PWC denoising methods, which, for example, combine the global behaviour of mean shift clustering with the local smoothing of total variation diffusion, and show example solver algorithms for these new methods. Comparisons between these methods are performed on synthetic and real signals, revealing that our new methods have a useful role to play. Finally, overlaps between the generalized methods of these two papers and others such as wavelet shrinkage, hidden Markov models, and piecewise smooth filtering are touched on.

ECG Noise Removal using GA tuned Sign-Data Least Mean Square Algorithm

Adaptive filter is a primary method to filter Electrocardiogram (ECG), because it does not need the signal statistical characteristics. In this paper, an adaptive filtering technique for denoising the ECG based on Genetic Algorithm (GA) tuned Sign-Data Least Mean Square (SD-LMS) algorithm is proposed. This technique minimizes the mean-squared error between the primary input, which is a noisy ECG, and a reference input which can be either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Noise is used as the reference signal in this work. The algorithm was applied to the records from the MIT -BIH Arrhythmia database for removing the baseline wander and 60Hz power line interference. The proposed algorithm gave an average signal to noise ratio improvement of 10.75 dB for baseline wander and 24.26 dB for power line interference which is better than the previous reported works

Modified level set method with Canny operator for image noise removal

The level set method is commonly used to address image noise removal. Existing studies concentrate mainly on determining the speed function of the evolution equation. Based on the idea of a Canny operator, this letter introduces a new method of controlling the level set evolution, in which the edge strength is taken into account in choosing curvature flows for the speed function and the normal to edge direction is used to orient the diffusion of the moving interface. The addition of an energy term to penalize the irregularity allows for better preservation of local edge information. In contrast with previous Canny-based level set methods that usually adopt a two-stage framework, the proposed algorithm can execute all the above operations in one process during noise removal.

A parallel method for impulsive image noise removal on hybrid CPU/GPU systems

A parallel algorithm for image noise removal is proposed. The algorithm is based on peer group concept and uses a fuzzy metric. An optimization study on the use of the CUDA platform to remove impulsive noise using this algorithm is presented. Moreover, an implementation of the algorithm on multi-core platforms using OpenMP is presented. Performance is evaluated in terms of execution time and a comparison of the implementation parallelised in multi-core, GPUs and the combination of both is conducted. A performance analysis with large images is conducted in order to identify the amount of pixels to allocate in the CPU and GPU. The observed time shows that both devices must have work to do, leaving the most to the GPU. Results show that parallel implementations of denoising filters on GPUs and multi-cores are very advisable, and they open the door to use such algorithms for real-time processing.

Image Noise Removal on Heterogeneous CPU-GPU Configurations

A parallel algorithm to remove impulsive noise in digital images using heterogeneous CPU/GPU computing is proposed. The parallel denoising algorithm is based on the peer group concept and uses an Euclidean metric. In order to identify the amount of pixels to be allocated in multi-core and GPUs, a performance analysis using large images is presented. A comparison of the parallel implementation in multi-core, GPUs and a combination of both is performed. Performance has been evaluated in terms of execution time and Megapixels/second. We present several optimization strategies especially effective for the multi-core environment, and demonstrate significant performance improvements. The main advantage of the proposed noise removal methodology is its computational speed, which enables efficient filtering of color images in real-time applications.

Brittle and ductile removal modes observed during diamond turning of carbon nanotube composites

Ultraprecision diamond turning was used to evaluate the surface integrity of a carbon nanotube (CNT) composite as a function of the cutting conditions and the percentage of CNT in the epoxy matrix. The effects of cutting conditions on the chip morphology and surface roughness were analysed. The results showed that an increase in the percentage of CNT may influence the mechanism of material removal and consequently improve the quality of the machined surface. When smaller quantities of CNT (0.02 and 0.07 wt %) are present in the matrix, microcracks form within the cutting grooves (perpendicular to the cutting direction). This indicates that the amount of CNT on the epoxy matrix may have a direct influence on the mechanical properties of these materials. Chips removed from the CNT composite samples were analysed by scanning electron microscopy in order to correlate the material removal mechanism and the surface generation process. The area average surface roughness Sa was influenced by the material removal mechanism (Sa ranging from 0.28 to 1.1 mu m).

Influence of feed time and sulfate load on the organic and sulfate removal in an ASBR

The removal of sulfate and organic matter was assessed in an ASBR, which treated wastewater containing 500 mg COD L(-1) (3 g COD L(-1) d(-1)) in 8 h-cycles at 30 degrees C. The wastewater was enriched with sulfate at [COD/SO(4)(2-]) ratios of 1.34, 0.67 and 0.34 (8.8,4.5 and 2.2 gSO(4)(2-) L(-1) d(-1)). For each COD/[SO(4)(2-)] ratio fill times used were: 10 min (batch), 3 and 6 h (fed-batch), achieving sulfate reduction of 30%, 72% and 72% (COD/[SO(4)(2-)] of 1.34); 25%, 58% and 55% (COD/[SO(4)(2-)] of 0.67) and 23%, 37% and 27% (COD/[SO(4)(2-)] of 0.34), respectively, and organic matter removal of 87%, 68% and 80% (COD/[SO(4)(2-)] of 1.34); 78%, 75% and 69% (COD/[SO(4)(2-)] of 0.67) and 85%, 84% and 83% (COD/[SO(4)(2-)] of 0.34), respectively. The results showed that fed-batch operation improved sulfate reduction, whereas organic matter removals were similar for batch and fed-batch operation. In addition, increase in sulfate loading in the fed-batch operation improved organic matter removal. (C) 2010 Elsevier Ltd. All rights reserved.

Three filter beds for arsenite and chromate removal

Arsenic (As) and chromium (Cr) are two of the most toxic pollutants introduced into natural waters from a variety of sources, and they cause various adverse effects on living bodies when their concentrations exceed permissible limits. Laboratory experiments have been conducted to investigate the sorption of As and Cr on carbon steel and removal of trace elements from drinking water with a household filtration process. The affinity of As and Cr species for iron/iron carbide (Fe/Fe3C) sites is the key factor in controlling the removal of the elements. The method is based on the use of powder carbon steel, powdered block carbon, and ball ceramic in the ion-sorption columns as a cleaning process. The presence of carbon steel in a system that contains As3+ and Cr6+ might have a potential effect.

A new approach to current and noise in double quantum dot systems

We use a quantum master equation to describe transport in double-dot devices. The coherent dot-to-dot coupling affects the noise spectra strongly. For phonon-assisted tunneling, the calculated current spectra are consistent with those of experiments. The model shows that quantum stochastic theory may he applied to some advantage in mesoscopic electronic systems. (C) 2000 Elsevier Science B.V. All rights reserved.

Simultaneous nitrification, denitrification, and phosphorus removal in a lab-scale sequencing batch reactor

Simultaneous nitrification and denitrification (SND) via the nitrite pathway and anaerobic-anoxic-enhanced biological phosphorus removal (EBPR) are two processes that can significantly reduce the energy and COD demand for nitrogen and phosphorus removal. The combination of these two processes has the potential of achieving simultaneous nitrogen and phosphorus removal with a minimal requirement for COD. A lab-scale sequencing batch reactor (SBR) was operated in alternating anaerobic-aerobic mode with a low dissolved oxygen (DO) concentration (0.5 mg/L) during the aerobic period, and was demonstrated to accomplish nitrification, denitrification, and phosphorus removal. Under anaerobic conditions, COD was taken up and converted to poly-hydroxyalkanoates (PHAs), accompanied by phosphorus release. In the subsequent aerobic stage, PHA was oxidized and phosphorus was taken up to