基于区域子块矩阵的多重正则化与生物医学图像重建

S. K. Biswas, K. Rajan, R. Vasu
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引用次数: 1

摘要

研究了一种基于区域信息的多重正则化求解生物逆问题。反问题通常采用牛顿法及其变体进行优化和求解。基于微积分的优化是极局部化的。结石的区域梯度对局部生理变化更为重要。提出了一种基于子块的生物漫射光学层析成像高斯-牛顿方法的多重正则化方法。研究了单步正则化(STR)方法和提出的基于子块的多重正则化方法。重建图像的分析结果表明,该方法有明显的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Regional sub-block matrices based multiple regularization and biomedical image reconstruction
A regional information based multiple regularization is studied for solving biological inverse problem. Inverse problems are usually optimized and solved by Newtons method and its variants. Optimization based on calculus is extremely localized. The regional gradient in calculus is more important for local physiological changes. A sub-block based multiple regularization is proposed in Gauss-Newtons method for biological diffuse optical tomograph (DOT). A study of single step regularization (STR) method and proposed subblock based multiple regularization method has been carried out. The reconstructed image analysis shows a significant improvement in the proposed method.
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