# Multi-wavelet residual dense convolutional neural network for image denoising

arXiv: https://arxiv.org/abs/2002.08301 · HTML: https://mlsanity.com/paper/2002.08301/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2020.

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

- [MWQ: Multiscale Wavelet Quantized Neural Networks](https://mlsanity.com/paper/2103.05363/) (2021)
- [Optimizing Sparse Convolution on GPUs with CUDA for 3D Point Cloud Processing in Embedded Systems](https://mlsanity.com/paper/2402.07710/) (2024)
- [ReTiDe: Real-Time Denoising for Energy-Efficient Motion Picture Processing with FPGAs](https://mlsanity.com/paper/2510.03812/) (2025)

## Abstract (arXiv)

Networks with large receptive field (RF) have shown advanced fitting ability in recent years. In this work, we utilize the short-term residual learning method to improve the performance and robustness of networks for image denoising tasks. Here, we choose a multi-wavelet convolutional neural network (MWCNN), one of the state-of-art networks with large RF, as the backbone, and insert residual dense blocks (RDBs) in its each layer. We call this scheme multi-wavelet residual dense convolutional neural network (MWRDCNN). Compared with other RDB-based networks, it can extract more features of the object from adjacent layers, preserve the large RF, and boost the computing efficiency. Meanwhile, this approach also provides a possibility of absorbing advantages of multiple architectures in a single network without conflicts. The performance of the proposed method has been demonstrated in extensive experiments with a comparison with existing techniques.