# Improved CycleGAN for Mixed Noise Removal in Infrared Images
###### Abstract  
Infrared images are susceptible to interference from a variety of factors during acquisition and transmission, resulting in the inclusion of mixed noise, which seriously affects the accuracy of subsequent vision tasks. To solve this problem, we designed a mixed noise removal algorithm for infrared images based on improved CycleGAN. First, we proposed a ResNet-E Block that incorporates the EMA (Efficient Multi-Scale Attention Module) and build a generator based on it using the skip-connection structure to improve the network's ability to remove mixed noise of different strengths. Second, we added the PSNR (Peak Signal-to-Noise Ratio) as an extra calculation item of cycle consistency loss, so that the network can effectively retain the detailed information of infrared images while denoising. Finally, we conducted experimental validation on both synthetic noisy images and real noisy images, which proved that our algorithm can effectively remove the mixed noise in infrared images and the denoising effect is better than other similar methods. infrared image denoising; CycleGAN; mixed noise; EMA +
Footnote †: journal: Medical Imaging  
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neighboring real pixels of an image and process the pixel values directly on the original image to recover the information masked by noise, and the typical algorithms mainly include mean filtering [7], median filtering [8], Gaussian filtering [9] and bilateral filtering [10]; the transform-based methods remove noise by transforming the image into the frequency domain or wavelet domain and inverting the result back to the image domain by processing the relevant parameters, and representative algorithms mainly include Discrete Fourier Transform [11], Wavelet Transform [12] and Discrete Cosine Transform [13]; image denoising methods based on the spatial combined with transform method achieve better denoising effect by combining the relevant algorithms in the spatial and transform domains, which comprehensively utilize the advantages of both to overcome the limitations of each. It mainly includes the BM3D (Block Matching and 3D collaborative filtering) [14] algorithm and the WNNM (Weighted Nuclear Norm Minimization) [15] algorithm. Although the traditional methods started earlier and provided a variety of generalized algorithms for the denoising task of infrared images for a long time, with the proposal of deep learning networks and their wide application in the field of image processing, algorithms based on deep learning have further pushed forward the development of denoising methods for infrared images and gradually become the mainstream of the current denoising approaches [16]. Compared with traditional algorithms, methods based on deep learning achieve better denoising results and the models obtained through training have a greater ability to generalize the inference of new concepts and get good performance on unfamiliar data or tasks. However, these methods still suffer from ineffective removal of mixed noise and loss of image texture details due to denoising. In order to address the above shortcomings, some researchers have made improvements on the original deep learning networks. For example, [17] proposed an infrared image noise removal network called the DMD-CNN (Deep Multi-scale Dense connection Convolutional Neural Network), which contains a multi-scale feature representation unit that can decompose the image into different scales for effective utilization of image information.