Laplacian Prior Variational Automatic Relevance Determination for Transmission Tomography
This work addresses a specific issue in transmission tomography reconstruction for medical or imaging applications, but it is incremental as it builds on existing sparsity methods.
The authors tackled the problem of choosing the penalty weight in L-1 sparsity-driven reconstruction by proposing Lap-VARD, which optimizes this weight using a Laplacian prior and automatic relevance determination, resulting in improved balance between sparsity and accuracy in transmission tomography.
In the classic sparsity-driven problems, the fundamental L-1 penalty method has been shown to have good performance in reconstructing signals for a wide range of problems. However this performance relies on a good choice of penalty weight which is often found from empirical experiments. We propose an algorithm called the Laplacian variational automatic relevance determination (Lap-VARD) that takes this penalty weight as a parameter of a prior Laplace distribution. Optimization of this parameter using an automatic relevance determination framework results in a balance between the sparsity and accuracy of signal reconstruction. Our algorithm is implemented in a transmission tomography model with sparsity constraint in wavelet domain.