Total Variation Iterative Linear Expansion of Thresholds with Applications in CT

Shahar Tsiper, Oren Solomon, Yonina C. Eldar

Abstract: The iterative linear expansion of threshold framework, or iLET, offers a new approach for solving image restoration problems under sparsity assumptions. Instead of estimating the reconstructed image directly, the iLET paradigm parametrizes the reconstruction process as a linear combination of elementary thresholding functions and optimizes over their coefficients. Here, we rely on the fast and accurate convergence of iLET, and propose an extension of this framework, under the assumption that the reconstructed object is approximately piece-wise constant. This assumption leads to a new total-variation framework of iLET. We demonstrate the applicability of our technique to bio-medical imaging problems, such as computerized tomography reconstruction. Our technique surpasses state-of-the-art reconstructions in terms of PSNR and SSIM, while offering an automatic way for tuning its regularization parameter.