![[2602.19314] IPv2: An Improved Image Purification Strategy for Real-World Ultra-Low-Dose Lung CT Denoising](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.19314] IPv2: An Improved Image Purification Strategy for Real-World Ultra-Low-Dose Lung CT Denoising
Summary
The paper presents IPv2, an enhanced image purification strategy for improving lung CT denoising at ultra-low doses, addressing limitations of previous methods.
Why It Matters
This research is significant as it tackles the critical issue of image quality in low-dose lung CT scans, which is essential for accurate diagnosis while minimizing patient radiation exposure. The proposed method enhances denoising capabilities, potentially leading to better clinical outcomes.
Key Takeaways
- IPv2 improves denoising in both background and lung tissue regions.
- The method introduces three core modules for effective image purification.
- Extensive experiments show consistent improvements across multiple denoising models.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.19314 (cs) [Submitted on 22 Feb 2026] Title:IPv2: An Improved Image Purification Strategy for Real-World Ultra-Low-Dose Lung CT Denoising Authors:Guoliang Gong, Man Yu View a PDF of the paper titled IPv2: An Improved Image Purification Strategy for Real-World Ultra-Low-Dose Lung CT Denoising, by Guoliang Gong and Man Yu View PDF HTML (experimental) Abstract:The image purification strategy constructs an intermediate distribution with aligned anatomical structures, which effectively corrects the spatial misalignment between real-world ultra-low-dose CT and normal-dose CT images and significantly enhances the structural preservation ability of denoising models. However, this strategy exhibits two inherent limitations. First, it suppresses noise only in the chest wall and bone regions while leaving the image background untreated. Second, it lacks a dedicated mechanism for denoising the lung parenchyma. To address these issues, we systematically redesign the original image purification strategy and propose an improved version termed IPv2. The proposed strategy introduces three core modules, namely Remove Background, Add noise, and Remove noise. These modules endow the model with denoising capability in both background and lung tissue regions during training data construction and provide a more reasonable evaluation protocol through refined label construction at the testing stage. Extensive experiments on our ...
