# Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network

arXiv: https://arxiv.org/abs/1707.09938 · HTML: https://mlsanity.com/paper/1707.09938/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2017.

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

- [Low-Dose CT Denoising Using a Structure-Preserving Kernel Prediction Network](https://mlsanity.com/paper/2105.14758/) (2021)

## Abstract (arXiv)

Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the texture were not fully recovered. To address this problem, here we propose a novel framelet-based denoising algorithm using wavelet residual network which synergistically combines the expressive power of deep learning and the performance guarantee from the framelet-based denoising algorithms. The new algorithms were inspired by the recent interpretation of the deep convolutional neural network (CNN) as a cascaded convolution framelet signal representation. Extensive experimental results confirm that the proposed networks have significantly improved performance and preserves the detail texture of the original images.