An Improved Denoising Model Based on the Analysis K-SVD Algorithm

Gong, Wenru; Li, Hongyi; Zhao, Debin

doi:10.1007/s00034-017-0496-7

Cited by 16 publications

(9 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The key steps of k-SVD algorithms are as follow. 35,36 Algorithm parameters: n is block size, k is dictionary size, J is the number of training iterations, λ is the language multiplier, and C is noise gain.…”

Section: Stationary Clutter Suppression Stationary Clutter Suppressio...mentioning

confidence: 99%

See 1 more Smart Citation

Scheme design of the optimal background clutter suppression for impulse radio ultrawide band radar in complex fire environment: a simulation and experimental study

Wang

Weng

et al. 2023

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

.Impulse radio ultrawide band (IR-UWB) radar possesses several advantages, such as heat flow insensitivity, high resolution, great penetration, good mobility, and lightweight, making it a promising technology for detecting and rescuing individuals trapped in fire scenarios. However, reliable detection and extraction of vital signs of trapped people in the presence of complex fire environment, such as water droplets, smoke, wall occlusions, and reflections, require an optimal background clutter suppression scheme designed specifically for fire scenarios. This study comprehensively evaluates 20 background noise suppression schemes using an exhaustive approach that involves constructing radar echo simulation signals and designing an evaluation method for algorithms. The practical applicability of the improved design solutions was evaluated by conducting small-scale fire experiments in an acrylic chamber. The results indicate that the designed scheme is effective in overcoming the background interference from the combustion environment and is capable of supporting subsequent trajectory identification and vital sign extraction in the fire scene environment.

show abstract

Section: Stationary Clutter Suppression Stationary Clutter Suppressio...mentioning

confidence: 99%

“…A number of improved K-SVD algorithms have been developed to solve various engineering problems after 34 who presented the K-SVD algorithm-generalizing the K-means clustering process firstly. The key steps of k-SVD algorithms are as follow 35 , 36 …”

Section: Optimal Background Clutter Suppression Scheme Designmentioning

confidence: 99%

Scheme design of the optimal background clutter suppression for impulse radio ultrawide band radar in complex fire environment: a simulation and experimental study

Wang

Weng

et al. 2023

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

show abstract

“…To select an appropriate regularization parameter, this method relies on various unbiased risk estimators according to the prior knowledge on the noise statistics. Other sophisticated methods based on neural network dictionary learning require considerable training data, and often involve many iterations to solve non-linear minimization problems [25,33]. Apparently, those methods are computationally rather intense.…”

Section: B Low-rank Approximationmentioning

confidence: 99%

Denoising scheme based on singular-value decomposition for one-dimensional spectra and its application in precision storage-ring mass spectrometry

et al. 2019

View full text Add to dashboard Cite

This work concerns noise reduction for one-dimensional spectra in the case that the signal is corrupted by an additive white noise. The proposed method starts with mapping the noisy spectrum to a partial circulant matrix. In virtue of singular-value decomposition of the matrix, components belonging to the signal are determined by inspecting the total variations of left singular vectors. Afterwards, a smoothed spectrum is reconstructed from the low-rank approximation of the matrix consisting of the signal components only. The de-noising effect of the proposed method is shown to be highly competitive among other existing non-parametric methods including moving average, wavelet shrinkage, and total variation. Furthermore, its applicable scenarios in precision storage-ring mass spectrometry are demonstrated to be rather diverse and appealing.

show abstract

“…In most cases, it needs to be coupled with a priori sparsity range. And it takes longer for multiple iterations to obtain the results [19,20]. erefore, the adaptive improvement for KSVD and target signal has become a hot research topic.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Sparsity Estimation KSVD Dictionary Construction Method for Compressed Sensing of Transient Signal

Han

Yang

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Overcomplete dictionaries occupy an essential position in the sparse representation of signals. The dictionary construction method typically represented by K singular value decomposition (KSVD) is widely used because of its concise and efficient features. In this paper, based on the background of transient signal detection, an adaptive sparse estimation KSVD (ASE-KSVD) dictionary training method is proposed to solve the redundant iteration problem caused by fixed sparsity in existing KSVD dictionary construction. The algorithm features an adaptive sparsity estimation strategy in the sparse coding stage, which adjusts the number of iterations required for dictionary training based on the pre-estimation of the sample signal characteristics. The aim is to decrease the number of solutions of the underdetermined system of equations, reduce the overfitting error under the finite sparsity condition, and improve overall training efficiency. We compare four similar algorithms under the speech signal and actual shock wave sensor network data conditions, respectively. The results show that the proposed algorithm has obvious performance advantages and can be applied to real-life scenarios.

show abstract

An Improved Denoising Model Based on the Analysis K-SVD Algorithm

Cited by 16 publications

References 29 publications

Scheme design of the optimal background clutter suppression for impulse radio ultrawide band radar in complex fire environment: a simulation and experimental study

Scheme design of the optimal background clutter suppression for impulse radio ultrawide band radar in complex fire environment: a simulation and experimental study

Denoising scheme based on singular-value decomposition for one-dimensional spectra and its application in precision storage-ring mass spectrometry

An Adaptive Sparsity Estimation KSVD Dictionary Construction Method for Compressed Sensing of Transient Signal

Contact Info

Product

Resources

About