Projection and interpolation based techniques for structured and impulsive noise filtering

In this chapter we present the relevant mathematical background to address two well defined signal and image processing problems. Namely, the problem of structured noise filtering and the problem of interpolation of missing data. The former is addressed by recourse to oblique projection based techniques whilst the latter, which can be considered equivalent to impulsive noise filtering, is tackled by appropriate interpolation methods.

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.

Estimation of signals in colored non gaussian noise based on Gaussian Mixture models

Non-Gaussianity of signals/noise often results in significant performance degradation for systems, which are designed using the Gaussian assumption. So non-Gaussian signals/noise require a different modelling and processing approach. In this paper, we discuss a new Bayesian estimation technique for non-Gaussian signals corrupted by colored non Gaussian noise. The method is based on using zero mean finite Gaussian Mixture Models (GMMs) for signal and noise. The estimation is done using an adaptive non-causal nonlinear filtering technique. The method involves deriving an estimator in terms of the GMM parameters, which are in turn estimated using the EM algorithm. The proposed filter is of finite length and offers computational feasibility. The simulations show that the proposed method gives a significant improvement compared to the linear filter for a wide variety of noise conditions, including impulsive noise. We also claim that the estimation of signal using the correlation with past and future samples leads to reduced mean squared error as compared to signal estimation based on past samples only.

Mathematical analysis for perceived annoyance of impulsive sounds in terms of physical factors

From the customer satisfaction point of view, sound quality of any product has become one of the important factors these days. The primary objective of this research is to determine factors which affect the acceptability of impulse noise. Though the analysis is based on a sample impulse sound file of a Commercial printer, the results can be applied to other similar impulsive noise. It is assumed that impulsive noise can be tuned to meet the accepTable criteria. Thus it is necessary to find the most significant factors which can be controlled physically. This analysis is based on a single impulse. A sample impulsive sound file is tweaked for different amplitudes, background noise, attack time, release time and the spectral content. A two level factorial design of experiments (DOE) is applied to study the significant effects and interactions. For each impulse file modified as per the DOE, the magnitude of perceived annoyance is calculated from the objective metric developed recently at Michigan Technological University. This metric is based on psychoacoustic criteria such as loudness, sharpness, roughness and loudness based impulsiveness. Software called ‘Artemis V11.2’ developed by HEAD Acoustics is used to calculate these psychoacoustic terms. As a result of two level factorial analyses, a new objective model of perceived annoyance is developed in terms of above mentioned physical parameters such as amplitudes, background noise, impulse attack time, impulse release time and the spectral content. Also the effects of the significant individual factors as well as two level interactions are also studied. The results show that all the mentioned five factors affect annoyance level of an impulsive sound significantly. Thus annoyance level can be reduced under the criteria by optimizing the levels. Also, an additional analysis is done to study the effect of these five significant parameters on the individual psychoacoustic metrics.

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.