On selection of spatial-varying regularization parameters in total variation image restoration

Direction of arrival (DOA) estimation has always been a hot topic for researchers. The complex and changeable environment makes it very challenging to estimate the DOA in a small snapshot and strong noise environment. The direction-of-arrival estimation method based on compressed sensing (CS) is a new method proposed in recent years. It has received widespread attention because it can realize the direction-of-arrival estimation under small snapshots. However, this method will cause serious distortion in a strong noise environment. To solve this problem, this paper proposes a DOA estimation algorithm based on the principle of CS and density-based spatial clustering (DBSCAN). First of all, in order to make the estimation accuracy higher, this paper selects a signal reconstruction strategy based on the basis pursuit de-noising (BPDN). In response to the challenge of the selection of regularization parameters in this strategy, the power spectrum entropy is proposed to characterize the noise intensity of the signal, so as to provide reasonable suggestions for the selection of regularization parameters; Then, this paper finds out that the DOA estimation based on the principle of CS will get a denser estimation near the real angle under the condition of small snapshots through analysis, so it is proposed to use a DBSCAN method to process the above data to obtain the final DOA estimate; Finally, calculate the cluster center value of each cluster, the number of clusters is the number of signal sources, and the cluster center value is the final DOA estimate. The proposed method is applied to the simulation experiment and the micro electro mechanical system (MEMS) vector hydrophone lake test experiment, and they are proved that the proposed method can obtain good results of DOA estimation under the conditions of small snapshots and low signal-to-noise ratio (SNR).

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Selection Of Regularization Parameters Of Inverse Problem By Computer Simulation Model

MMET 96 VIth International Conference on Mathematical Methods in Electromagnetic Theory Proceedings MMET-96 ◽

10.1109/mmet.1996.565766 ◽

2005 ◽

Author(s):

B. Iavors'kyi ◽

T. Rafa

Keyword(s):

Inverse Problem ◽

Computer Simulation ◽

Simulation Model ◽

Computer Simulation Model ◽

Regularization Parameters ◽

Selection Of

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2D Wavelet Tree Ordering Based Localized Total Variation Model for Efficient Image Restoration

International Journal of Image and Graphics ◽

10.1142/s0219467822400095 ◽

2022 ◽

Author(s):

K. Praveen Kumar ◽

C. Venkata Narasimhulu ◽

K. Satya Prasad

Keyword(s):

Image Restoration ◽

Total Variation ◽

Good Condition ◽

Window Size ◽

Local Variation ◽

Image Simulation ◽

Regularization Algorithm ◽

Total Variation Model ◽

Degraded Image ◽

Adjustment Scheme

The degraded image during the process of image analysis needs more number of iterations to restore it. These iterations take long waiting time and slow scanning, resulting in inefficient image restoration. A few numbers of measurements are enough to recuperate an image with good condition. Due to tree sparsity, a 2D wavelet tree reduces the number of coefficients and iterations to restore the degraded image. All the wavelet coefficients are extracted with overlaps as low and high sub-band space and ordered them such that they are decomposed in the tree ordering structured path. Some articles have addressed the problems with tree sparsity and total variation (TV), but few authors endorsed the benefits of tree sparsity. In this paper, a spatial variation regularization algorithm based on tree order is implemented to change the window size and variation estimators to reduce the loss of image information and to solve the problem of image smoothing operation. The acceptance rate of the tree-structured path relies on local variation estimators to regularize the performance parameters and update them to restore the image. For this, the Localized Total Variation (LTV) method is proposed and implemented on a 2D wavelet tree ordering structured path based on the proposed image smooth adjustment scheme. In the end, a reliable reordering algorithm proposed to reorder the set of pixels and to increase the reliability of the restored image. Simulation results clearly show that the proposed method improved the performance compared to existing methods of image restoration.

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