# Wavelet Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1805.08620 · HTML: https://mlsanity.com/paper/1805.08620/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2018.

## Built on by (most cited first, 29 of 29)

- [Wave-ViT: Unifying Wavelet and Transformers for Visual Representation Learning](https://mlsanity.com/paper/2207.04978/) (2022)
- [Wavelet Convolutions for Large Receptive Fields](https://mlsanity.com/paper/2407.05848/) (2024)
- [AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis](https://mlsanity.com/paper/2112.12744/) (2021)
- [A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends](https://mlsanity.com/paper/2402.15490/) (2024)
- [SpectralNET: Exploring Spatial-Spectral WaveletCNN for Hyperspectral Image Classification](https://mlsanity.com/paper/2104.00341/) (2021)
- [WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2408.01231/) (2024)
- [WaMo: Wavelet-Enhanced Multi-Frequency Trajectory Analysis for Fine-Grained Text-Motion Retrieval](https://mlsanity.com/paper/2508.03343/) (2025)
- [ProtoHPE: Prototype-guided High-frequency Patch Enhancement for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2310.07552/) (2023)
- [Learnable Discrete Wavelet Pooling (LDW-Pooling) For Convolutional Networks](https://mlsanity.com/paper/2109.06638/) (2021)
- [Wavelet Regularization Benefits Adversarial Training](https://mlsanity.com/paper/2206.03727/) (2022)
- [Multiscale Training of Convolutional Neural Networks](https://mlsanity.com/paper/2501.12739/) (2025)
- [Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images](https://mlsanity.com/paper/2212.00330/) (2022)
- [Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN](https://mlsanity.com/paper/2212.13161/) (2022)
- [Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition](https://mlsanity.com/paper/2409.09216/) (2024)
- [Efficient Hybrid Network: Inducting Scattering Features](https://mlsanity.com/paper/2203.15392/) (2022)
- [DePF: A Novel Fusion Approach based on Decomposition Pooling for Infrared and Visible Images](https://mlsanity.com/paper/2305.17376/) (2023)
- [A Structurally Regularized Convolutional Neural Network for Image Classification using Wavelet-based SubBand Decomposition](https://mlsanity.com/paper/2103.01823/) (2021)
- [VisTaNet: Attention Guided Deep Fusion for Surface Roughness Classification](https://mlsanity.com/paper/2209.08516/) (2022)
- [WavPool: A New Block for Deep Neural Networks](https://mlsanity.com/paper/2306.08734/) (2023)
- [WCCNet: Wavelet-context Cooperative Network for Efficient Multispectral Pedestrian Detection](https://mlsanity.com/paper/2308.01042/) (2023)
- [Subspace Clustering in Wavelet Packets Domain](https://mlsanity.com/paper/2406.03819/) (2024)
- [Spectral Wavelet Dropout: Regularization in the Wavelet Domain](https://mlsanity.com/paper/2409.18951/) (2024)
- [RW-Net: Enhancing Few-Shot Point Cloud Classification with a Wavelet Transform Projection-based Network](https://mlsanity.com/paper/2501.03221/) (2025)
- [Adaptive Wavelet Filters as Practical Texture Feature Amplifiers for Parkinson's Disease Screening in OCT](https://mlsanity.com/paper/2503.19292/) (2025)
- [WT-BCP: Wavelet Transform based Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2504.14445/) (2025)
- [Multi-Scale Target-Aware Representation Learning for Fundus Image Enhancement](https://mlsanity.com/paper/2505.01831/) (2025)
- [MEGANet-W: A Wavelet-Driven Edge-Guided Attention Framework for Weak Boundary Polyp Detection](https://mlsanity.com/paper/2507.02668/) (2025)
- [Conceptualizing Multi-scale Wavelet Attention and Ray-based Encoding for Human-Object Interaction Detection](https://mlsanity.com/paper/2507.10977/) (2025)
- [WTHaar-Net: a Hybrid Quantum-Classical Approach](https://mlsanity.com/paper/2603.02497/) (2026)

## Abstract (arXiv)

Spatial and spectral approaches are two major approaches for image processing tasks such as image classification and object recognition. Among many such algorithms, convolutional neural networks (CNNs) have recently achieved significant performance improvement in many challenging tasks. Since CNNs process images directly in the spatial domain, they are essentially spatial approaches. Given that spatial and spectral approaches are known to have different characteristics, it will be interesting to incorporate a spectral approach into CNNs. We propose a novel CNN architecture, wavelet CNNs, which combines a multiresolution analysis and CNNs into one model. Our insight is that a CNN can be viewed as a limited form of a multiresolution analysis. Based on this insight, we supplement missing parts of the multiresolution analysis via wavelet transform and integrate them as additional components in the entire architecture. Wavelet CNNs allow us to utilize spectral information which is mostly lost in conventional CNNs but useful in most image processing tasks. We evaluate the practical performance of wavelet CNNs on texture classification and image annotation. The experiments show that wavelet CNNs can achieve better accuracy in both tasks than existing models while having significantly fewer parameters than conventional CNNs.