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

## TL;DR

This paper introduces a new infrared image denoising model that improves noise removal while preserving image details using wavelet transform and self-attention techniques.

## Contribution

The novel WTEIDM model integrates wavelet transform with self-attention and multi-scale gating for enhanced infrared image denoising.

## Key findings

- WTEIDM outperforms existing denoising methods in PSNR and SSIM metrics across multiple infrared datasets.
- The model demonstrates robustness and generalization under varying noise levels (σ = 15, 25, 50).

## Abstract

Infrared images are often degraded by complex noise due to hardware and environmental factors, posing challenges for subsequent processing and target detection. To overcome the shortcomings of existing denoising methods in balancing noise removal and detail preservation, this paper proposes a Wavelet Transform Enhanced Infrared Denoising Model (WTEIDM). Firstly, a Wavelet Transform Self-Attention (WTSA) is designed, which combines the frequency-domain decomposition ability of the discrete wavelet transform (DWT) with the dynamic weighting mechanism of self-attention to achieve effective separation of noise and detail. Secondly, a Multi-Scale Gated Linear Unit (MSGLU) is devised to improve the ability to capture detail information and dynamically control features through dual-branch multi-scale depth-wise convolution and gating strategy. Finally, a Parallel Hybrid Attention Module (PHAM) is proposed to enhance cross-dimensional feature fusion effect through the parallel cross-interaction of spatial and channel attention. Extensive experiments are conducted on five infrared datasets under different noise levels (σ = 15, 25, and 50). The results demonstrate that the proposed WTEIDM outperforms several state-of-the-art denoising algorithms on both PSNR and SSIM metrics, confirming its superior generalization capability and robustness.