Multidimensional Filter Banks and Multiscale Geometric Representations

Minh, N.; Lu, Yue M.

doi:10.1561/2000000012

Cited by 14 publications

(2 citation statements)

References 101 publications

(149 reference statements)

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“…It is known that at the most basic level, animal echolocation depends on the neural anatomy of the auditory circuits of the brain. In modern works devoted to this topic, e.g., [21][22][23][24], the locational abilities of animals are recorded, but the mechanisms (procedures) of signal processing by the animals themselves that allow the implementing of these abilities are not explained, and the question of how locational abilities are implemented on the neural operational basis inherent in living organisms is not raised.…”

Section: Introductionmentioning

confidence: 99%

Novel Neuron-like Procedure of Weak Signal Detection against the Non-Stationary Noise Background with Application to Underwater Sound

et al. 2022

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The well-known method of detecting a useful signal in the presence of noise during underwater remote sensing, based on the matched filtering of the received signal with the test signal, provides the maximum signal-to-noise ratio (SNR) at the receiver output. To do this, a correlation-type criterion function (CF) is constructed for the received and test signals. In the case of large volumes of processed data, this method requires the use of large computing resources. The search for a data processing method with lower computational costs, as well as the effective application of artificial neural networks to array signal processing, motivates the authors to propose an alternative approach to the CF construction based on the McCulloch–Pitts neuron model. Such a neuron-like CF is based on a specific nonlinear transformation of the input and test signals and uses only logical operations, which require much less computational resources. The ratio of the output signal amplitude to the input noise level is indeed the maximum with matched filtering. Studies have shown that it is not this parameter that should be considered, but statistical characteristics, on the basis of which the thresholds for detecting a signal in the presence of noise are determined. Such characteristics include the probability density distributions of correlation and neuron-like CFs in the presence and absence of noise. In this case, the signal detection thresholds will be lower for the neuron-like CF than for the conventional correlation CF. The aim of this research is to increase the accuracy of the selection of a useful signal against the intense noise background when using a processor based on the neuron-like CF and to determine the conditions when the input SNR, at which signal detection is possible, is lower compared to the correlation CF. The comparative results of stochastic modeling show the effectiveness of using a new neuron-like approach to reduce the detection threshold when a chirp signal is received against a background of unsteady Gaussian noise. The advantages of the neuron-like method become significant when the statistical distribution of the additive noise does not change, but its variance increases or decreases. In order to confirm the presence of non-stationarity in real noises, experimental data obtained from the remote sounding of bottom sediments in the Black Sea are presented. The results obtained are considered to be applicable in a wide range of practical situations related to remote sensing in non-stationary environments, long-range sonar and sea bottom exploration.

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Section: Introductionmentioning

confidence: 99%

Novel Neuron-like Procedure of Weak Signal Detection against the Non-Stationary Noise Background with Application to Underwater Sound

et al. 2022

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“…In [4], authors represent directional image features using directional filter banks (DFBs) by dividing the 2-D frequency spectra with wedge-shaped partitions. Although DFBs do not provide multiscale representation, they have been key in widely recognized contourlet family [5] and hybrid wavelets and directional filter banks (HWD) transform in [6]. A comprehensive coverage of previously proposed MGTs is given in [5,7].…”

Section: Introductionmentioning

confidence: 99%

Image denoising using redundant finer directional wavelet transform

Gajbhar

Joshi

2013

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)

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In this paper, we propose two designs of redundant finer directional wavelet transform (FiDWT) and explain its application to image denoising. 2-channel perfect reconstruction (PR) checkerboard-shaped filter bank (CSFB) is at the core of the designs. The 2-channel CSFB, uses 2-D nonseparable analysis and synthesis filter responses without downsampling/upsampling matrices resulting in redundancy factor of 2. Both these designs have two lowpass and six highpass directional subbands. The hard-thresholding results for image denoising using proposed designs clearly shows improvement in PSNR as well as visual quality of the denoised images. Using the Bayes least squaresGaussian scale mixture (BLS-GSM), a current state-of-the-art wavelet-based image denoising technique with the proposed two times redundant FiDWT design indicates encouraging results on textural images with much less computational cost.Index Terms-Wavelet transform, multidimensional filter banks, image denoising, BLS-GSM.

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References

2019

Multidimensional Signal and Color Image Processing Using Lattices

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Multidimensional Filter Banks and Multiscale Geometric Representations

Cited by 14 publications

References 101 publications

Novel Neuron-like Procedure of Weak Signal Detection against the Non-Stationary Noise Background with Application to Underwater Sound

Novel Neuron-like Procedure of Weak Signal Detection against the Non-Stationary Noise Background with Application to Underwater Sound

Image denoising using redundant finer directional wavelet transform

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