论文标题
参数级别增强以改善重建（palentir）
Parametric Level-sets Enhanced To Improve Reconstruction (PaLEnTIR)
论文作者
论文摘要
我们介绍了Palentir，这是一种显着增强的参数级别集（PAL）方法，该方法解决了分段常数对象的恢复和重建。我们的关键贡献涉及使用单个级别功能的独特的PALS配方，以还原包含多对比的分段恒定对象的场景，而无需了解对象数量或其对比度。与采用径向基函数（RBF）的标准PAL方法不同，我们的模型集成了各向异性基函数（ABF），从而扩大了其代表更广泛形状的能力。此外，Palentir改善了作为参数识别过程的一部分所需的Jacobian矩阵的调节，因此加速了优化方法。我们通过包括稀疏和有限的视角X射线计算机断层扫描（2D和3D），非线性弥散光学层析成像（DOT），DENOISING和DENOCONTORLOUTION任务，使用真实和模拟的数据集来验证Palentir的功效。
We introduce PaLEnTIR, a significantly enhanced parametric level-set (PaLS) method addressing the restoration and reconstruction of piecewise constant objects. Our key contribution involves a unique PaLS formulation utilizing a single level-set function to restore scenes containing multi-contrast piecewise-constant objects without requiring knowledge of the number of objects or their contrasts. Unlike standard PaLS methods employing radial basis functions (RBFs), our model integrates anisotropic basis functions (ABFs), thereby expanding its capacity to represent a wider class of shapes. Furthermore, PaLEnTIR improves the conditioning of the Jacobian matrix, required as part of the parameter identification process, and consequently accelerates optimization methods. We validate PaLEnTIR's efficacy through diverse experiments encompassing sparse and limited angle of view X-ray computed tomography (2D and 3D), nonlinear diffuse optical tomography (DOT), denoising, and deconvolution tasks using both real and simulated data sets.
