# Multi-Scale Wavelet Domain Residual Learning for Limited-Angle CT Reconstruction

arXiv: https://arxiv.org/abs/1703.01382 · HTML: https://mlsanity.com/paper/1703.01382/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2017.

## Built on by (most cited first, 3 of 3)

- [Self-Attention Generative Adversarial Network for Iterative Reconstruction of CT Images](https://mlsanity.com/paper/2112.12810/) (2021)
- [Robustness of data-driven approaches in limited angle tomography](https://mlsanity.com/paper/2403.11350/) (2024)
- [Wavelet-Optimized Pseudo-3D Accelerated Diffusion Model for Truncated Computed Laminography](https://mlsanity.com/paper/2606.31318/) (2026)

## Abstract (arXiv)

Limited-angle computed tomography (CT) is often used in clinical applications such as C-arm CT for interventional imaging. However, CT images from limited angles suffers from heavy artifacts due to incomplete projection data. Existing iterative methods require extensive calculations but can not deliver satisfactory results. Based on the observation that the artifacts from limited angles have some directional property and are globally distributed, we propose a novel multi-scale wavelet domain residual learning architecture, which compensates for the artifacts. Experiments have shown that the proposed method effectively eliminates artifacts, thereby preserving edge and global structures of the image.