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[2602.21987] PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for medical images
Summary
PatchDenoiser introduces a lightweight, multi-scale denoising framework for medical images, effectively reducing noise while preserving anatomical details.
Why It Matters
Medical imaging is crucial for accurate diagnosis and treatment, yet noise can significantly hinder image quality. PatchDenoiser offers a practical solution that balances performance and efficiency, making it a valuable tool for healthcare professionals and researchers in the field.
Key Takeaways
- PatchDenoiser is designed to efficiently denoise medical images while maintaining fine details.
- It significantly reduces model parameters and energy consumption compared to traditional deep learning methods.
- The framework demonstrates robust performance across various imaging conditions and scanner types.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.21987 (cs) [Submitted on 25 Feb 2026] Title:PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for medical images Authors:Jitindra Fartiyal, Pedro Freire, Sergei K. Turitsyn, Sergei G. Solovski View a PDF of the paper titled PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for medical images, by Jitindra Fartiyal and 3 other authors View PDF HTML (experimental) Abstract:Medical images are essential for diagnosis, treatment planning, and research, but their quality is often degraded by noise from low-dose acquisition, patient motion, or scanner limitations, affecting both clinical interpretation and downstream analysis. Traditional filtering approaches often over-smooth and lose fine anatomical details, while deep learning methods, including CNNs, GANs, and transformers, may struggle to preserve such details or require large, computationally expensive models, limiting clinical practicality. We propose PatchDenoiser, a lightweight, energy-efficient multi-scale patch-based denoising framework. It decomposes denoising into local texture extraction and global context aggregation, fused via a spatially aware patch fusion strategy. This design enables effective noise suppression while preserving fine structural and anatomical details. PatchDenoiser is ultra-lightweight, with far fewer parameters and lower computational complexity than CNN-, GAN-, a...