# Image reconstruction from few views by L0-norm optimization

arXiv: https://arxiv.org/abs/1401.1882 · HTML: https://mlsanity.com/paper/1401.1882/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2014.

## Built on by (most cited first, 1 of 1)

- [DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction](https://mlsanity.com/paper/2404.17890/) (2024)

## Abstract (arXiv)

The L1-norm of the gradient-magnitude images (GMI), which is the well-known total variation (TV) model, is widely used as regularization in the few views CT reconstruction. As the L1-norm TV regularization is tending to uniformly penalize the image gradient and the low-contrast structures are sometimes over smoothed, we proposed a new algorithm based on the L0-norm of the GMI to deal with the few views problem. To rise to the challenges introduced by the L0-norm DGT, the algorithm uses a pseudo-inverse transform of DGT and adapts an iterative hard thresholding (IHT) algorithm, whose convergence and effective efficiency have been theoretically proven. The simulation indicates that the algorithm proposed in this paper can obviously improve the reconstruction quality.