A single-pixel compressive imaging technique that uses differential modulation based on the transformation of discrete orthogonal Krawtchouk moments is proposed. In this method, two sets of Krawtchouk basis patterns are used to differentially modulate the light source, then the Krawtchouk moments of the target object are acquired from the light intensities measured by a single-pixel detector. The target image is reconstructed by applying an inverse Krawtchouk moment transform represented in the matrix form. The proposed technique is verified by both computational simulations and laboratory experiments. The results show that this technique can retrieve an image from compressive measurements and the real-time reconstruction. The background noise can be removed by the differential measurement to realize the excellent image quality. Moreover, the proposed technique is especially suitable for the single-pixel imaging application that requires the extraction of the characteristics at the region-of-interest.
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http://dx.doi.org/10.1364/OE.27.029838 | DOI Listing |
Med Vet Entomol
December 2023
Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia.
By introducing parameters with local information, several types of orthogonal moments have recently been developed for the extraction of local features in an image. But with the existing orthogonal moments, local features cannot be well-controlled with these parameters. The reason lies in that zeros distribution of these moments' basis function cannot be well-adjusted by the introduced parameters.
View Article and Find Full Text PDFPeerJ Comput Sci
September 2021
Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia.
In image analysis, orthogonal moments are useful mathematical transformations for creating new features from digital images. Moreover, orthogonal moment invariants produce image features that are resistant to translation, rotation, and scaling operations. Here, we show the result of a case study in biological image analysis to help researchers judge the potential efficacy of image features derived from orthogonal moments in a machine learning context.
View Article and Find Full Text PDFJ Acoust Soc Am
September 2021
Department of Speech-Language-Hearing Sciences, University of Minnesota, Minneapolis, Minnesota 55455, USA.
An objective metric that predicts speech intelligibility under different types of noise and distortion would be desirable in voice communication. To date, the majority of studies concerning speech intelligibility metrics have focused on predicting the effects of individual noise or distortion mechanisms. This study proposes an objective metric, the spectrogram orthogonal polynomial measure (SOPM), that attempts to predict speech intelligibility for people with normal hearing under adverse conditions.
View Article and Find Full Text PDFEntropy (Basel)
September 2021
Department of Computer Engineering, Interdisciplinary Research Center for Intelligent Secure Systems, Research Institute, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
Krawtchouk polynomials (KPs) and their moments are promising techniques for applications of information theory, coding theory, and signal processing. This is due to the special capabilities of KPs in feature extraction and classification processes. The main challenge in existing KPs recurrence algorithms is that of numerical errors, which occur during the computation of the coefficients in large polynomial sizes, particularly when the KP parameter () values deviate away from 0.
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