In unmanned aerial vehicle (UAV) image-processing applications, one needs to implement different parallel image-enhancement algorithms on several high-performance computing platforms utilizing various programming mode...
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In unmanned aerial vehicle (UAV) image-processing applications, one needs to implement different parallel image-enhancement algorithms on several high-performance computing platforms utilizing various programming models. To speed up the parallelization procedure and improve its efficiency, the automatic parallel software package, Par4All, is applied in this work. We find that the performance of the original automatic parallelization algorithm produced with Par4All is inefficient. To resolve this problem, we propose different optimization approaches for Par4All based on Intel (R)'s Xeon Phi high-performance computing platform that are based on the structural features of the image-enhancement algorithms, which can further optimize the original parallel algorithm. These approaches mainly include: (1) Par4All automatic parallel search module optimization, (2) dynamic thread setting optimization, and (3) the collaborative parallelization of both CPU and many integrated core (MIC) processors. According to the results of the comparison experiments involving different algorithms, it is shown that the proposed optimization approaches for these kinds of algorithms can significantly improve the performance of automatic parallel algorithms. The acceleration ratio increases approximately by 30%, 70%, and 80% for the multiscale Retinex, Gaussian-filtering and median-filtering algorithms, respectively. As continuation and deepening of our previous research work, this research has the potential to be beneficial for other researchers in image-processing applications with image-enhancement algorithms.
Speech enhancement has been an intensive research for several decades to enhance the noisy speech that is corrupted by additive noise, multiplicative noise or convolutional noise. Even after decades of research it is ...
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Speech enhancement has been an intensive research for several decades to enhance the noisy speech that is corrupted by additive noise, multiplicative noise or convolutional noise. Even after decades of research it is still the most challenging problem, because most papers rely on estimating the noise during the nonspeech activity assuming that the background noise is uncorrelated (statistically independent of speech signal), nonstationary and slowly varying, so that the noise characteristics estimated in the absence of speech can be used subsequently in the presence of speech, whereas in a real time environment such assumptions do not hold for all the time. In this paper, we discuss the historical development of approaches that starts from the year 1970 to, the recent, 2013 for enhancing the noisy speech corrupted by additive background noise. Seeing the history, there are algorithms that enhance the noisy speech very well as long as a specific application is concerned such as the In-car noisy environments. It has to be observed that a speech enhancement algorithm performs well with a good estimation of the noise Power Spectral Density (PSD) from the noisy speech. Our idea pops up based on this observation, for online speech enhancement (i.e. in a real time environment) such as mobile phone applications, instead of estimating the noise from the noisy speech alone, the system should be able to monitor an environment continuously and classify it. Based on the current environment of the user, the system should adapt the algorithm (i.e. enhancement or estimation algorithm) for the current environment to enhance the noisy speech.
A new automatic target detection method for IR images that only requires information about the size of the targets is described. The. proposed nonlinear-enhancement-based system (NLEBS) algorithm is based on a nonline...
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A new automatic target detection method for IR images that only requires information about the size of the targets is described. The. proposed nonlinear-enhancement-based system (NLEBS) algorithm is based on a nonlinear enhancement paradigm that increases the contrast of the targets with minimal change in the clutter's contrast. The NLEBS employs several stages of processing, each with a different operational purpose. First, the nonlinear enhancement operation is performed by using an iterative procedure. After binarization, segmentation merging causes each local image region to grow by filling in holes. Then segmentation pruning is applied to remove spurious segments. Finally, a heuristic-based metric is employed to validate the possible targets. The performance of the NLEBS was tested with a targe set of IR images. The results of these experiments showed a probability of detection greater than 90% and a false-alarm rate of about 1 false alarm per image. (C) 2000 Society of Photo-Optical Instrumentation Engineers.
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