Dr. Zhiwen Liu Profile

Dr. Zhiwen Liu

at Institute of Remote Sensing and Digital Earth

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Proceedings Article | 24 September 2011 Paper

Tree structure matching pursuit based on Gaussian scale mixtures model

Peng Liu, Zhiwen Liu, Jingbo Wei, Dingsheng Liu

Proceedings Volume 8135, 813520 (2011) https://doi.org/10.1117/12.903684

KEYWORDS: Reconstruction algorithms, Compressed sensing, Wavelets, Global system for mobile communications, Signal processing, Detection theory, Image processing, Image restoration, Algorithms, Inverse problems

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Compressed Sensing (CS) theory has gained so much attention recently in the areas of signal processing. The sparsity of the transform coefficients has been widely employed in the early CS recovery techniques. However, except for the sparsity, there are other priors about transform coefficients such as the tree structure and the statistical dependencies that could be employed in CS reconstruction. In this paper, we propose to introduce the Gaussian Scale Mixtures (GSM) model into the tree structure based Orthogonal Matching Pursuit (TSOMP) reconstruction algorithm. This GSM model can efficiently denote the statistical dependencies between wavelet coefficients. And these statistical dependencies will improve the accuracy of the searching of the tree structure subspace in TSOMP algorithm. When both the inter-scale dependences (such as GSM model) of the coefficients and the intra-scale dependences (such as tree structure) of the coefficients are combined into the Orthogonal Matching Pursuit reconstruction algorithm, the noise and instability in TSOMP reconstruction are well reduced. Some state-of-the-art methods are compared with the proposed method. Experimental results show that the proposed method improves reconstruction accuracy for a given number of measurements and more image details are recovered.

Proceedings Article | 23 October 2010 Paper

Total variation restoration of the defocus image based on spectral priors

Peng Liu, Dingsheng Liu, Zhiwen Liu

Proceedings Volume 7830, 783018 (2010) https://doi.org/10.1117/12.864662

KEYWORDS: Image restoration, Image sensors, Remote sensing, Sensors, Satellites, Dysprosium, Iterative methods, Satellite imaging, Multispectral imaging, Earth observing sensors

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In this article, we de-blur one of the out of focus image among several multispectral (MS) remote sensing images by total variation method. The no blur images are used as priors in the restoration of the out of focus image. Although the distributions of the pixel intensity of the multimodal image of different CCD sensors are greatly different form each other, the directions of their edges are very similar. Then, these similar structures and edge information are used as the important priors or constraints in the total variation image restoration. The steps are: first, the PAN (panchromatic) image is denoted approximately as the weighted sum of all the bands of MS images, and the weight parameters of the relationship between the PAN image and the MS images are computed by least square method; Second by the relationship and the weight parameters, an initial estimation of the out of focus image is calculated; third, the total variation image restoration is local linearized by fixed point iterative method; fourth, the initial estimation for the out of focus image in the third step is brought to the fixed point iteration. At last, by introduce the new priors from the relationship between MS and PAN image, the new total variation image restoration frame is constructed. The edge and gradient information from the no blur images of other channels make the total variation regularization better suppress the noise in de-convolution. The comprehensive experiments are done by using different images with different level of noise. The higher PSNR is acquired by proposed method when it is compared with some other state of art methods. Experiments confirm that the algorithm is very effective especially when the noise in blur remote sensing image is relative large.

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