# Limited-angle CT reconstruction via the L1/L2 minimization

arXiv: https://arxiv.org/abs/2006.00601 · HTML: https://mlsanity.com/paper/2006.00601/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2020.

## Built on by (most cited first, 12 of 12)

- [Deep Microlocal Reconstruction for Limited-Angle Tomography](https://mlsanity.com/paper/2108.05732/) (2021)
- [Minimizing L1 over L2 norms on the gradient](https://mlsanity.com/paper/2101.00809/) (2021)
- [LRIP-Net: Low-Resolution Image Prior based Network for Limited-Angle CT Reconstruction](https://mlsanity.com/paper/2208.00207/) (2022)
- [An Efficient Smoothing and Thresholding Image Segmentation Framework with Weighted Anisotropic-Isotropic Total Variation](https://mlsanity.com/paper/2202.10115/) (2022)
- [SDIP: Self-Reinforcement Deep Image Prior Framework for Image Processing](https://mlsanity.com/paper/2404.12142/) (2024)
- [Quaternion Nuclear Norms Over Frobenius Norms Minimization for Robust Matrix Completion](https://mlsanity.com/paper/2504.21468/) (2025)
- [Difference of Anisotropic and Isotropic TV for Segmentation under Blur and Poisson Noise](https://mlsanity.com/paper/2301.03393/) (2023)
- [ADMM for Structured Fractional Minimization](https://mlsanity.com/paper/2411.07496/) (2024)
- [Transformed ℓ1 Gradient Regularization for Image Denoising](https://mlsanity.com/paper/2511.15060/) (2025)
- [Limited-Angle CT Reconstruction Using Multi-Volume Latent Consistency Model](https://mlsanity.com/paper/2603.05183/) (2026)
- [Low-Rank Tensor Completion Based on Fractional Regularization with Ky Fan p-k Norm](https://mlsanity.com/paper/2606.19046/) (2026)
- [Separable Nonnegative Matrix Factorization Using Powered Ratio-of-Norms Regularization](https://mlsanity.com/paper/2608.28799/) (2026)

## Abstract (arXiv)

In this paper, we consider minimizing the L1/L2 term on the gradient for a limited-angle scanning problem in computed tomography (CT) reconstruction. We design a specific splitting framework for an unconstrained optimization model so that the alternating direction method of multipliers (ADMM) has guaranteed convergence under certain conditions. In addition, we incorporate a box constraint that is reasonable for imaging applications, and the convergence for the additional box constraint can also be established. Numerical results on both synthetic and experimental datasets demonstrate the effectiveness and efficiency of our proposed approaches, showing significant improvements over the state-of-the-art methods in the limited-angle CT reconstruction.