###### Abstract
The authors of another study placed the non-local correlation into the sparse representation. They designed a proper sub-director and sub-sparse vector to improve recognition ability and to achieve a high peak signal-to-noise ratio (PSNR) [13]. Before denoising, a great deal of work must be conducted [14]. K-means singular value decomposition (KSVD) was used to solve the principal component analysis (PCA) problem. The method is not convex optimization, and it cannot obtain a globally optimal solution [15]. The over-complete dictionary to learn was adopted. Through a redundant dictionary, a better sparse effect could be achieved. It is more robust in a complex environment [16]. The nuclear norm model was used to represent sparsity. It is the slack approximate of the convex optimization and it has a better convergence effect on denoising, but it ignores and obscures some details. Article [17] considers the meaning of the eigenvalue and uses the WNNM model to strengthen the sparsity to achieve better convergence. The important details and texture are kept with the higher complexity. PSNR is better than \(1\sim 3\) dB in some real images and test images compared with the nuclear norm model [18]. The nonlocal similarity and the whole sparsity were taken into consideration. The sparsity definitions of IR images were optimized for the denoising effect. But It is of higher complexity. The authors proposed a new denoising method called EMD-ITF that was based on empirical mode decomposition (EMD) and the improved thresholding function (ITF). An improved threshold is used to suppress noise and to improve the signal-to-noise ratio (SNR) [19]. The SNR of the denoising signal exceeds the original signal with \(5\sim 9\) dB. [PERSON] adopted a reliable method to identify compound faults in bearings when the availability of experimental data was limited [20]. This can detect compound faults with 100% ten-fold cross-validation accuracy. This is used in some forms of digital signal processing and is suitable for specific signals with noise. Deep learning is widely used in visible light image denoising, hyperspectral image denoising, and high-resolution image denoising. It is rarely used in infrared data sets. A large number of annotated data sets is needed to utilize hyper-spectrum images and high-resolution images for denoising. DrCNN [21] integrates local and global features with residual dense blocks in a deeper convolutional neural network (CNN) in image recovery, where more robust characteristics are required. FFDNet [22] uses a non-uniform noise level map as the input and runs on down-sampled sub-images. The method achieves a better trade-off between computation ability and denoising performance in synthetic and real noisy images. An attention-guided denoising convolutional neural network (ADNet) contains a sparse block, a feature enhancement block, an attention block, and a reconstruction block [23]. The influences of the shallow layers on deep layers could be enhanced. However, with a high level of noisy images, it suffers from rapid performance degradation. The article [24] proposes a multi-stage image denoising CNN with the wavelet transform to further remove redundant features. The process refines the obtained features and reconstructs the clean image with improved residual dense architectures. The authors of another study proposed a trust-based security system [25]. This was utilized to balance the security, transmission performance, and energy efficiency. One article proposed an energy-cost-per-useful-bit (ECPUB) method [26]. ECPUB can evaluate the energy efficiency and facilitate the balance of network load. A trust management-based and low-energy-adaptive clustering hierarchy protocol outperformed it in prolonging the network lifetime and in balancing energy consumption [27]. As is known, the number of publicly-annotated infrared data sets is relatively small. The different levels of noise mostly need different CNN models.