Efficient regularization parameter estimation based incomplete orthogonalization method

2009 ◽  
Author(s):  
Kai Xie
Keyword(s):  
Parameter Estimation ◽  
Regularization Parameter ◽  
Orthogonalization Method

scholarly journals Iteratively Reweighted Blind Deconvolution With Adaptive Regularization Parameter Estimation

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 11959-11973 ◽  
Author(s):  
Houzhang Fang ◽  
Yi Chang ◽  
Gang Zhou ◽  
Lizhen Deng
Keyword(s):  
Parameter Estimation ◽  
Blind Deconvolution ◽  
Regularization Parameter ◽  
Adaptive Regularization

scholarly journals Regularization Parameter Estimation for Non-Negative Hyperspectral Image Deconvolution

2016 ◽  
Vol 25 (11) ◽  
pp. 5316-5330 ◽  
Author(s):  
Yingying Song ◽  
David Brie ◽  
El-Hadi Djermoune ◽  
Simon Henrot
Keyword(s):  
Parameter Estimation ◽  
Hyperspectral Image ◽  
Regularization Parameter ◽  
Image Deconvolution

scholarly journals SIMRES-TV: NOISE AND RESIDUAL SIMILARITY FOR PARAMETER ESTIMATION IN TOTAL VARIATION

2021 ◽  
Vol XLIV-2/W1-2021 ◽  
pp. 171-176
Author(s):  
V. B. S. Prasath ◽  
N. N. Hien ◽  
D. N. H. Thanh ◽  
S. Dvoenko
Keyword(s):  
Image Processing ◽  
Parameter Estimation ◽  
Image Restoration ◽  
Total Variation ◽  
Regularization Parameter ◽  
Noise Levels ◽  
Split Bregman ◽  
Edge Preserving ◽  
Restoration Scheme ◽  
Split Bregman Algorithm

Abstract. Image restoration with regularization models is very popular in the image processing literature. Total variation (TV) is one of the important edge preserving regularization models used, however, to obtain optimal restoration results the regularization parameter needs to be set appropriately. We propose here a new parameter estimation approach for total variation based image restoration. By utilizing known noise levels we compute the regularization parameter by reducing the similarity between residual and noise variances. We use the split Bregman algorithm for the total variation along with this automatic parameter estimation step to obtain a very fast restoration scheme. Experimental results indicate the proposed parameter estimation obtained better denoised images and videos in terms of PSNR and SSIM measures and the computational overload is less compared with other approaches.

scholarly journals Efficient Parameter Estimation for Sparse SAR Imaging Based on Complex Image and Azimuth-Range Decouple

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4549
Author(s):  
Mingqian Liu ◽  
Bingchen Zhang ◽  
Zhongqiu Xu ◽  
Yirong Wu
Keyword(s):  
Parameter Estimation ◽  
Large Scale ◽  
Regularization Parameter ◽  
Estimation Method ◽  
Imaging Method ◽  
Adaptive Parameter ◽  
Processing Theory ◽  
Complex Image ◽  
Sar Imaging ◽  
Observation Matrix

Sparse signal processing theory has been applied to synthetic aperture radar (SAR) imaging. In compressive sensing (CS), the sparsity is usually considered as a known parameter. However, it is unknown practically. For many functions of CS, we need to know this parameter. Therefore, the estimation of sparsity is crucial for sparse SAR imaging. The sparsity is determined by the size of regularization parameter. Several methods have been presented for automatically estimating the regularization parameter, and have been applied to sparse SAR imaging. However, these methods are deduced based on an observation matrix, which will entail huge computational and memory costs. In this paper, to enhance the computational efficiency, an efficient adaptive parameter estimation method for sparse SAR imaging is proposed. The complex image-based sparse SAR imaging method only considers the threshold operation of the complex image, which can reduce the computational costs significantly. By utilizing this feature, the parameter is pre-estimated based on a complex image. In order to estimate the sparsity accurately, adaptive parameter estimation is then processed in the raw data domain, combining with the pre-estimated parameter and azimuth-range decouple operators. The proposed method can reduce the computational complexity from a quadratic square order to a linear logarithm order, which can be used in the large-scale scene. Simulated and Gaofen-3 SAR data processing results demonstrate the validity of the proposed method.

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