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Related Experiment Video

Updated: Jan 29, 2026

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

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Published on: February 15, 2022

Infrared Image Denoising Algorithm Based on Wavelet Transform and Self-Attention Mechanism.

Hongmei Li¹, Yang Zhang¹, Luxia Yang¹

¹College of Computer Science and Technology, Taiyuan Normal University, Jinzhong 030619, China.

Sensors (Basel, Switzerland)

|January 28, 2026

Summary

This summary is machine-generated.

This study introduces a Wavelet Transform Enhanced Infrared Denoising Model (WTEIDM) to improve infrared image quality. The WTEIDM effectively removes noise while preserving crucial details, outperforming existing methods.

Keywords:

infrared image denoising multi-scale gated linear unit parallel hybrid attention transformer wavelet transform self-attention

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Area of Science:

Computer Vision
Image Processing
Signal Processing

Background:

Infrared images suffer from complex noise, hindering target detection and analysis.
Existing denoising methods struggle to balance noise reduction with detail preservation.

Purpose of the Study:

To propose an advanced infrared denoising model that effectively removes noise and preserves image details.
To enhance the performance of infrared image processing for subsequent tasks.

Main Methods:

Developed a Wavelet Transform Enhanced Infrared Denoising Model (WTEIDM).
Incorporated a Wavelet Transform Self-Attention (WTSA) module for noise-detail separation.
Utilized a Multi-Scale Gated Linear Unit (MSGLU) for enhanced detail capture.
Employed a Parallel Hybrid Attention Module (PHAM) for improved feature fusion.

Main Results:

The WTEIDM demonstrated superior performance compared to state-of-the-art denoising algorithms.
Evaluated on five infrared datasets with varying noise levels (σ = 15, 25, 50).
Achieved higher Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics.

Conclusions:

The proposed WTEIDM effectively addresses the limitations of current infrared denoising techniques.
The model exhibits excellent generalization capability and robustness across different noise conditions.
WTEIDM significantly enhances infrared image quality for improved downstream applications.