# WaveSNet: Wavelet Integrated Deep Networks for Image Segmentation

arXiv: https://arxiv.org/abs/2005.14461 · HTML: https://mlsanity.com/paper/2005.14461/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2020.

## Built on by (most cited first, 10 of 10)

- [WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification](https://mlsanity.com/paper/2107.13335/) (2021)
- [WaveMix: A Resource-efficient Neural Network for Image Analysis](https://mlsanity.com/paper/2205.14375/) (2022)
- [M2SFormer: Multi-Spectral and Multi-Scale Attention with Edge-Aware Difficulty Guidance for Image Forgery Localization](https://mlsanity.com/paper/2506.20922/) (2025)
- [MWQ: Multiscale Wavelet Quantized Neural Networks](https://mlsanity.com/paper/2103.05363/) (2021)
- [One Model for All Quantization: A Quantized Network Supporting Hot-Swap Bit-Width Adjustment](https://mlsanity.com/paper/2105.01353/) (2021)
- [YNetr: Dual-Encoder architecture on Plain Scan Liver Tumors (PSLT)](https://mlsanity.com/paper/2404.00327/) (2024)
- [MEGANet-W: A Wavelet-Driven Edge-Guided Attention Framework for Weak Boundary Polyp Detection](https://mlsanity.com/paper/2507.02668/) (2025)
- [MFC-RFNet: A Multi-scale Guided Rectified Flow Network for Radar Sequence Prediction](https://mlsanity.com/paper/2601.03633/) (2026)
- [Bridging the Geometry Mismatch: Frequency-Aware Anisotropic Serialization for Thin-Structure SSMs](https://mlsanity.com/paper/2603.28503/) (2026)
- [ZMIS-SAM: Segment Anything Model Enhanced with Wavelet Transform for Zooplankton Microscopy Image Instance Segmentation](https://mlsanity.com/paper/2607.27585/) (2026)

## Abstract (arXiv)

In deep networks, the lost data details significantly degrade the performances of image segmentation. In this paper, we propose to apply Discrete Wavelet Transform (DWT) to extract the data details during feature map down-sampling, and adopt Inverse DWT (IDWT) with the extracted details during the up-sampling to recover the details. We firstly transform DWT/IDWT as general network layers, which are applicable to 1D/2D/3D data and various wavelets like Haar, Cohen, and Daubechies, etc. Then, we design wavelet integrated deep networks for image segmentation (WaveSNets) based on various architectures, including U-Net, SegNet, and DeepLabv3+. Due to the effectiveness of the DWT/IDWT in processing data details, experimental results on CamVid, Pascal VOC, and Cityscapes show that our WaveSNets achieve better segmentation performances than their vanilla versions.