A Denoising Method for Complex Background Noise of Infrared Imaging Guidance System Based on Deep Learning and Dual-domain Fusion
The infrared imaging guidance system is severely affected by harsh operating environments,and the imaging process is accompanied by complex background noise interference,which seriously affects the guidance and tracking accuracy of the system.In order to reduce the impact of composite noise on the infrared imaging effect,a priori setting of noise characteristics based on additive and multiplicative components is proposed based on the analysis of the causes and characteristics of various common noises;further,a denoising method for different types of noise based on deep convolutional neural network is designed according to the dual-domain fusion denoising idea of spatial domain and transform domain.This method introduces a rich gradient flow convolutional module into the UNet++structure to reduce the gradient information redundancy and enhance the multi-receptive field feature extraction ability.A dimension attention mechanism is proposed to achieve dual-domain noise estimation according to the noise morphology characteristics.The high-order dual-tree complex wavelet transform is introduced as the domain transformation method to improve the recognition ability of noise components at different scales and directions.The effectiveness and superiority of the noise prior setting and dual-domain fusion denoising idea are verified through ablation experiments,and the proposed method demonstrates the excellent denoising ability for various types of noise through comparative experiments.The proposed method achieves the peak signal-to-noise ratio of 29.57 and the structural similarity index of 0.85 for Gaussian noise removal,which is superior to other typical noise suppression methods.For multi-type mixed noise,it achieves the denoising levels of 27.84 and 0.82,respectively.Moreover,the proposed method was validated to possess significant capability in removing the noises from real image.
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