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Related Experiment Videos

Interferometric image denoising network SEVReNet.

Kunpeng Li¹, Rongli Guo¹, Siyi Wang¹

¹School of Optoelectronic Engineering, Xi'an Technological University, Xi'an, Shannxi 710021, China.

Biomedical Optics Express

|May 18, 2026

Summary

This summary is machine-generated.

SEVReNet, a novel self-supervised network, effectively denoises interferometric images with complex Gaussian and speckle noise. Its unique three-branch architecture achieves superior performance by preserving fine details and separating noise from structural information.

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Area of Science:

Optical Imaging
Image Processing
Deep Learning

Background:

Interferometric imaging suffers from complex noise, including Gaussian and speckle noise.
Existing deep learning denoising networks struggle with multiple superimposed noise sources due to limited generalization.

Purpose of the Study:

To propose SEVReNet, a self-supervised network for robust interferometric image denoising.
To address the limitations of current methods in handling complex, superimposed noise.

Main Methods:

Developed SEVReNet, a three-branch network incorporating translation, rotation, and scale equivariance.
Employed adaptive fusion through weighted integration of equivariant modules for noise separation and detail preservation.

Main Results:

SEVReNet achieved superior denoising performance on synthetic and real datasets compared to BM3D, U-Net, Restormer, and AdaReNet.
Achieved an average PSNR of 31.48 dB and SSIM of 0.924, demonstrating significant outperformance.

Conclusions:

SEVReNet proves robust and effective for denoising interferometric images under complex noise conditions.
The study offers new insights into noise modeling and image restoration for optical imaging.