Katamreddy Sree Harsha, Yalavarthy Phaneendra K
Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore 560 012, India.
J Opt Soc Am A Opt Image Sci Vis. 2012 May 1;29(5):649-56. doi: 10.1364/JOSAA.29.000649.
Diffuse optical tomographic imaging is known to be an ill-posed problem, and a penalty/regularization term is used in image reconstruction (inverse problem) to overcome this limitation. Two schemes that are prevalent are spatially varying (exponential) and constant (standard) regularizations/penalties. A scheme that is also spatially varying but uses the model information is introduced based on the model-resolution matrix. This scheme, along with exponential and standard regularization schemes, is evaluated objectively based on model-resolution and data-resolution matrices. This objective analysis showed that resolution characteristics are better for spatially varying penalties compared to standard regularization; and among spatially varying regularization schemes, the model-resolution based regularization fares well in providing improved data-resolution and model-resolution characteristics. The verification of the same is achieved by performing numerical experiments in reconstructing 1% noisy data involving simple two- and three-dimensional imaging domains.
已知扩散光学断层成像存在不适定问题，并且在图像重建（反问题）中使用惩罚/正则化项来克服这一局限性。两种常见的方案是空间变化（指数）正则化和常数（标准）正则化/惩罚。基于模型分辨率矩阵引入了一种同样是空间变化但使用模型信息的方案。该方案与指数正则化方案和标准正则化方案一起，基于模型分辨率矩阵和数据分辨率矩阵进行客观评估。这种客观分析表明，与标准正则化相比，空间变化惩罚的分辨率特性更好；在空间变化正则化方案中，基于模型分辨率的正则化在提供改进的数据分辨率和模型分辨率特性方面表现良好。通过对涉及简单二维和三维成像域的1%噪声数据进行重建的数值实验来验证这一点。
