Diffusion prior-based zero-shot denoising for real-world fluorescence microscopy images via encoder-decoder guided...

Area of Science:

• Microscopy and Imaging
• Computational Biology
• Image Processing

Background:

• Fluorescence microscopy is crucial for biological research but often yields noisy images due to technical constraints.
• Existing denoising methods struggle with the unique noise characteristics of fluorescence microscopy and the difficulty of obtaining paired noisy-clean training data.

Purpose of the Study:

• To develop a robust zero-shot denoising algorithm for fluorescence microscopy images that overcomes limitations of current methods.
• To address the challenge of real-world noise in fluorescence microscopy without requiring paired training data.

Main Methods:

• Proposes ED-Diff, a zero-shot denoising algorithm leveraging diffusion priors.
• Integrates inverse problem optimization with diffusion sampling.
• Introduces a novel noisy image transformation module (NiTM) with an encoder-decoder structure to handle diverse real-world noise.

Main Results:

• Extensive experiments on multiple real-world datasets demonstrate the effectiveness of the NiTM module.
• ED-Diff shows competitive and robust denoising performance across various noise scenarios.
• Validated the algorithm's ability to denoise images without paired noisy-clean data.

Conclusions:

• ED-Diff offers a significant advancement in denoising fluorescence microscopy images, particularly in real-world applications.
• The proposed NiTM module enhances the algorithm's adaptability to complex noise patterns.
• This method provides a practical solution for improving image quality in biological research where clean training data is scarce.