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Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

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Enhancing Microscopic Image Quality With DiffusionFormer and Crow Search Optimization.

Subhash Chandra Patel¹, Rajesh N Kamath², T S N Murthy³

¹School of Computing Science and Engineering, VIT Bhopal University, Bhopal, India.

Microscopy Research and Technique

|September 30, 2025

Summary

This summary is machine-generated.

This study introduces a novel denoising method using a diffusion process, swin transformer, and optimized crow search algorithm to enhance medical image quality. The technique significantly improves diagnostic accuracy, especially for conditions like pneumonia.

Keywords:

Denoising Swin transformer networks crow search optimization diffusion former diffusion process medical image

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Area of Science:

Medical Imaging
Artificial Intelligence
Image Processing

Background:

Medical image noise degrades diagnostic accuracy.
Effective denoising is crucial, particularly in low-resource settings.
Pneumonia diagnosis is challenging due to image similarity and data scarcity.

Purpose of the Study:

To introduce a novel denoising technique for medical images.
To enhance image quality and reliability for improved diagnoses.
To address challenges in diagnosing diseases like pneumonia using medical imaging.

Main Methods:

A novel Diffusion with swin transformer-based Optimized Crow Search algorithm was developed.
Preprocessing included grayscale conversion, resizing, normalization, and Gaussian noise addition.
The method integrates diffusion for noise reduction, swin transformer for feature capture, and crow search for hyperparameter optimization.

Main Results:

The method achieved a Peak Signal-to-Noise Ratio (PSNR) of 38.47 dB.
Structural Similarity Index Measure (SSIM) reached 98.14%, and Feature Similarity Index Measure (FSIM) was 0.980.
Mean Squared Error (MSE) was 0.55, outperforming existing denoising techniques across four diverse datasets.

Conclusions:

The proposed approach effectively enhances medical image quality.
This leads to more precise and dependable diagnoses.
The technique shows significant potential for improving diagnostic capabilities in various medical applications.