# WINNet: Wavelet-inspired Invertible Network for Image Denoising

arXiv: https://arxiv.org/abs/2109.06381 · HTML: https://mlsanity.com/paper/2109.06381/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2021.

## Built on by (most cited first, 30 of 34)

- [Reversible Vision Transformers](https://mlsanity.com/paper/2302.04869/) (2023)
- [Reversible Decoupling Network for Single Image Reflection Removal](https://mlsanity.com/paper/2410.08063/) (2024)
- [Semantic Deep Hiding for Robust Unlearnable Examples](https://mlsanity.com/paper/2406.17349/) (2024)
- [Imperceptible Adversarial Attack via Invertible Neural Networks](https://mlsanity.com/paper/2211.15030/) (2022)
- [Invertible Diffusion Models for Compressed Sensing](https://mlsanity.com/paper/2403.17006/) (2024)
- [Robust Time Series Denoising with Learnable Wavelet Packet Transform](https://mlsanity.com/paper/2206.06126/) (2022)
- [Dual-former: Hybrid Self-attention Transformer for Efficient Image Restoration](https://mlsanity.com/paper/2210.01069/) (2022)
- [Spatial-Frequency U-Net for Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2307.14648/) (2023)
- [A Lightweight Deep Exclusion Unfolding Network for Single Image Reflection Removal](https://mlsanity.com/paper/2503.01938/) (2025)
- [Task-Generalized Adaptive Cross-Domain Learning for Multimodal Image Fusion](https://mlsanity.com/paper/2508.15505/) (2025)
- [Emphasizing Crucial Features for Efficient Image Restoration](https://mlsanity.com/paper/2405.11468/) (2024)
- [INDigo: An INN-Guided Probabilistic Diffusion Algorithm for Inverse Problems](https://mlsanity.com/paper/2306.02949/) (2023)
- [INDIGO+: A Unified INN-Guided Probabilistic Diffusion Algorithm for Blind and Non-Blind Image Restoration](https://mlsanity.com/paper/2501.14014/) (2025)
- [Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained Vehicle Recognition](https://mlsanity.com/paper/2401.14336/) (2024)
- [DestripeCycleGAN: Stripe Simulation CycleGAN for Unsupervised Infrared Image Destriping](https://mlsanity.com/paper/2402.09101/) (2024)
- [SVASTIN: Sparse Video Adversarial Attack via Spatio-Temporal Invertible Neural Networks](https://mlsanity.com/paper/2406.01894/) (2024)
- [Exploring Richer and More Accurate Information via Frequency Selection for Image Restoration](https://mlsanity.com/paper/2407.08950/) (2024)
- [Generalized Task-Driven Medical Image Quality Enhancement with Gradient Promotion](https://mlsanity.com/paper/2501.01114/) (2025)
- [SMILENet: Unleashing Extra-Large Capacity Image Steganography via a Synergistic Mosaic InvertibLE Hiding Network](https://mlsanity.com/paper/2503.05118/) (2025)
- [RINS-T: Robust Implicit Neural Solvers for Time Series Linear Inverse Problems](https://mlsanity.com/paper/2510.17396/) (2025)
- [mSQUID: Model-Based Leanred Modulo Recovery at Low Sampling Rates](https://mlsanity.com/paper/2510.18729/) (2025)
- [Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks with Soft-Thresholding](https://mlsanity.com/paper/2304.06959/) (2023)
- [Towards Zero Memory Footprint Spiking Neural Network Training](https://mlsanity.com/paper/2308.08649/) (2023)
- [Generalizing to Out-of-Sample Degradations via Model Reprogramming](https://mlsanity.com/paper/2403.05886/) (2024)
- [Enhancing Perception Quality in Remote Sensing Image Compression via Invertible Neural Network](https://mlsanity.com/paper/2405.10518/) (2024)
- [LatentINDIGO: An INN-Guided Latent Diffusion Algorithm for Image Restoration](https://mlsanity.com/paper/2505.12935/) (2025)
- [Diffusion Model-based Activity Completion for AI Motion Capture from Videos](https://mlsanity.com/paper/2505.21566/) (2025)
- [Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2506.18270/) (2025)
- [Conceptualizing Multi-scale Wavelet Attention and Ray-based Encoding for Human-Object Interaction Detection](https://mlsanity.com/paper/2507.10977/) (2025)
- [Multi-resolution Enhancement for Full Spectrum Neural Representations](https://mlsanity.com/paper/2509.15494/) (2025)

## Abstract (arXiv)

Image denoising aims to restore a clean image from an observed noisy image. The model-based image denoising approaches can achieve good generalization ability over different noise levels and are with high interpretability. Learning-based approaches are able to achieve better results, but usually with weaker generalization ability and interpretability. In this paper, we propose a wavelet-inspired invertible network (WINNet) to combine the merits of the wavelet-based approaches and learningbased approaches. The proposed WINNet consists of K-scale of lifting inspired invertible neural networks (LINNs) and sparsity-driven denoising networks together with a noise estimation network. The network architecture of LINNs is inspired by the lifting scheme in wavelets. LINNs are used to learn a non-linear redundant transform with perfect reconstruction property to facilitate noise removal. The denoising network implements a sparse coding process for denoising. The noise estimation network estimates the noise level from the input image which will be used to adaptively adjust the soft-thresholds in LINNs. The forward transform of LINNs produce a redundant multi-scale representation for denoising. The denoised image is reconstructed using the inverse transform of LINNs with the denoised detail channels and the original coarse channel. The simulation results show that the proposed WINNet method is highly interpretable and has strong generalization ability to unseen noise levels. It also achieves competitive results in the non-blind/blind image denoising and in image deblurring.