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Multiresolution regularized least squares image reconstruction based on wavelet in optical tomography

机译:光学层析成像中基于小波的多分辨率正则最小二乘图像重建

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Abstract: In this paper, we present a wavelet based multigrid approach to solve the perturbation equation encountered in optical tomogrpahy. With this scheme, the unkown image, the data, as well as weight matrix are all represented by wavelet expansions, and thus yielding a multiresolution representation of the original perturbation equation in the wavelet domain. This transformed equation is then solved using multigrid scheme, by which an increasing portion of wavelet coefficients of the unknown image are solved in successive approximations. One can also quickly identify regions of interest from a coarse level reconstruction and restrict the reconstruction in the following fine resolutions to those regions. At each resolution level, a regularized least squares solution is obtained using a conjugate gradient descent method. Compared to a previously reported one grid algorithm, the multigrid method requires substantially shorter computation time under the same reconstruction quality criterion. !17
机译:摘要:在本文中,我们提出了一种基于小波的多重网格方法来解决光学层析成像中遇到的摄动方程。利用该方案,未知图像,数据以及权重矩阵都由小波展开表示,因此在小波域中产生了原始摄动方程的多分辨率表示。然后使用多网格方案求解该变换的方程,通过该方法以逐次逼近的方式求解未知图像的小波系数的增加部分。人们还可以从粗略水平的重建中快速识别出感兴趣的区域,并以以下精细分辨率将重建限制在这些区域。在每个分辨率级别,使用共轭梯度下降方法获得正则化最小二乘解。与先前报道的一种网格算法相比,在相同的重建质量标准下,多网格方法需要显着缩短的计算时间。 !17

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