This work is unique in the use of discrete wavelets that were built from or derived from Chebyshev polynomials of the second and third kind, filter the Discrete Second Chebyshev Wavelets Transform (DSCWT), and derive two effective filters. The Filter Discrete Third Chebyshev Wavelets Transform (FDTCWT) is used in the process of analyzing color images and removing noise and impurities that accompany the image, as well as because of the large amount of data that makes up the image as it is taken. These data are massive, making it difficult to deal with each other during transmission. However to address this issue, the image compression technique is used, with the image not losing information due to the readings that were obtained, and the results were satisfactory. Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), Bit Per Pixel (BPP), and Compression Ratio (CR) Coronavirus is the initial treatment, while the processing stage is done with network training for Convolutional Neural Networks (CNN) with Discrete Second Chebeshev Wavelets Convolutional Neural Network (DSCWCNN) and Discrete Third Chebeshev Wavelets Convolutional Neural Network (DTCWCNN) to create an efficient algorithm for face recognition, and the best results were achieved in accuracy and in the least amount of time. Two samples of color images that were made or implemented were used. The proposed theory was obtained with fast and good results; the results are evident shown in the tables below.

翻译：本研究的独特之处在于：利用由第二类和第三类切比雪夫多项式构造的离散小波，通过离散第二类切比雪夫小波变换(DSCWT)提取两个有效滤波器，并将离散第三类切比雪夫小波变换(FDTCWT)应用于彩色图像分析过程，以去除图像采集时伴随的噪声和杂质。由于图像包含大量数据，在传输过程中难以处理。为解决此问题，采用图像压缩技术，且根据获得的读数未造成图像信息丢失，结果令人满意。采用均方误差(MSE)、峰值信噪比(PSNR)、每像素比特数(BPP)和压缩比(CR)作为评估指标。初始处理阶段使用冠状病毒算法，而处理阶段通过卷积神经网络(CNN)与离散第二类切比雪夫小波卷积神经网络(DSCWCNN)及离散第三类切比雪夫小波卷积神经网络(DTCWCNN)进行网络训练，以构建高效的人脸识别算法，在最短时间内实现了最优准确率。研究使用了两组实施或生成的彩色图像样本，所提出的理论方法获得了快速且良好的结果，具体效果如下表所示。