We present a fundamentally new regularization method for the solution of the Fredholm integral equation of the first kind, in which we incorporate solutions corresponding to a range of Tikhonov regularizers into the end result. This method identifies solutions within a much larger function space, spanned by this set of regularized solutions, than is available to conventional regularizaton methods. Each of these solutions is regularized to a different extent. In effect, we combine the stability of solutions with greater degrees of regularization with the resolution of those that are less regularized. In contrast, current methods involve selection of a single, or in some cases several, regularization parameters that define an optimal degree of regularization. Because the identified solution is within the span of a set of differently-regularized solutions, we call this method \textit{span of regularizations}, or SpanReg. We demonstrate the performance of SpanReg through a non-negative least squares analysis employing a Gaussian basis, and demonstrate the improved recovery of bimodal Gaussian distribution functions as compared to conventional methods. We also demonstrate that this method exhibits decreased dependence of the end result on the optimality of regularization parameter selection. We further illustrate the method with an application to myelin water fraction mapping in the human brain from experimental magnetic resonance imaging relaxometry data. We expect SpanReg to be widely applicable as an effective new method for regularization of inverse problems.
翻译:我们为Fredholm第一种整体等式的解决方案提出了一个全新的新规范化方法,在其中,我们把与一系列Tikhoonov正规化者相对应的解决方案纳入最终结果。这个方法在比常规正规化方法大得多的功能空间中找到解决办法,其范围比常规常规正规化方法大得多。每种解决方案都在不同程度上正规化。实际上,我们把解决方案的稳定性和更高程度的正规化与较不正规的解决方案的解决方案相结合。相比之下,当前方法涉及选择一个单一或在某些情况下若干确定最佳正规化程度的正规化参数。由于确定的解决办法是在一套非正规化解决办法的范围之内,我们称这种方法为“常规化解决办法”的范围比常规常规方法大得多,我们称之为“常规化解决办法”的范围宽得多。我们通过使用高山基础的非负负负最小的最小方分析,展示了SpanRegg的绩效,并表明与常规方法相比,双调制高尚分配功能的恢复情况有所改善。我们还进一步表明,这种方法表明,在将最终的正规化方法作为最终的模型应用结果方面,我们从可比较性地展示了在将人类的模型的精确度上,我们将标准化的模型的模型用于对准化方法的模型的精确性选择。