# WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising

arXiv: https://arxiv.org/abs/2403.11672 · HTML: https://mlsanity.com/paper/2403.11672/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spots](https://mlsanity.com/paper/2203.06967/) (2022)
- [Wave-ViT: Unifying Wavelet and Transformers for Visual Representation Learning](https://mlsanity.com/paper/2207.04978/) (2022)
- [Patch-level Representation Learning for Self-supervised Vision Transformers](https://mlsanity.com/paper/2206.07990/) (2022)
- [Wavelet-Based Dual-Branch Network for Image Demoireing](https://mlsanity.com/paper/2007.07173/) (2020)
- [CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoising](https://mlsanity.com/paper/2202.13517/) (2022)
- [Cycle-free CycleGAN using Invertible Generator for Unsupervised Low-Dose CT Denoising](https://mlsanity.com/paper/2104.08538/) (2021)
- [ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising](https://mlsanity.com/paper/2307.12225/) (2023)

## Built on by (most cited first, 7 of 7)

- [Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentation](https://mlsanity.com/paper/2511.10945/) (2025)
- [Optimizing Lung Cancer Detection in CT Imaging: A Wavelet Multi-Layer Perceptron (WMLP) Approach Enhanced by Dragonfly Algorithm (DA)](https://mlsanity.com/paper/2408.15355/) (2024)
- [Projection Guided Personalized Federated Learning for Low Dose CT Denoising](https://mlsanity.com/paper/2603.13422/) (2026)
- [Physics-Driven Independent Pair Generation for Iterative Self-Supervised Low-Dose CT Denoising](https://mlsanity.com/paper/2609.02654/) (2026)
- [One-Step Is Optimal: Unconditional Rectified Flows are Noise2Noise Denoisers, and Multi-Step Integration Provably Hurts---A Benchmark and Task-Based Detectability Study on Low-Dose CT](https://mlsanity.com/paper/2609.31670/) (2026)
- [Anatomy-preserving unpaired cone-beam CT refinement for image-guided radiotherapy using pseudo-label guided diffusion](https://mlsanity.com/paper/2610.06094/) (2026)
- [Noise2Match: Self-Supervised Low-Dose CT Denoising via Adaptive Inter-Slice Matching](https://mlsanity.com/paper/2610.18379/) (2026)

## Abstract (arXiv)

In clinical examinations and diagnoses, low-dose computed tomography (LDCT) is crucial for minimizing health risks compared with normal-dose computed tomography (NDCT). However, reducing the radiation dose compromises the signal-to-noise ratio, leading to degraded quality of CT images. To address this, we analyze LDCT denoising task based on experimental results from the frequency perspective, and then introduce a novel self-supervised CT image denoising method called WIA-LD2ND, only using NDCT data. The proposed WIA-LD2ND comprises two modules: Wavelet-based Image Alignment (WIA) and Frequency-Aware Multi-scale Loss (FAM). First, WIA is introduced to align NDCT with LDCT by mainly adding noise to the high-frequency components, which is the main difference between LDCT and NDCT. Second, to better capture high-frequency components and detailed information, Frequency-Aware Multi-scale Loss (FAM) is proposed by effectively utilizing multi-scale feature space. Extensive experiments on two public LDCT denoising datasets demonstrate that our WIA-LD2ND, only uses NDCT, outperforms existing several state-of-the-art weakly-supervised and self-supervised methods. Source code is available at https://github.com/zhaohaoyu376/WI-LD2ND.