Abstract
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.
Abstract (translated)
这项工作的独特之处在于使用了从或基于Chebyshev多项式第二类和第三类的离散小波，过滤Discrete Second Chebyshev Wavelets Transform(DSCWT)，并推导出两个有效的过滤器。滤波Discrete Third Chebyshev Wavelets Transform(FDTCWT)用于分析彩色图像，并去除伴随图像的噪声和杂质，以及由于在拍摄时构成图像的大量数据。这些数据是巨大的，因此在传输期间很难相互处理。然而，为了解决这一问题，使用图像压缩技术，由于图像没有因获得的阅读而丢失信息，结果令人满意。Mean Square Error(MSE)、Peak Signal-to-Noise Ratio(PSNR)、每个像素的比特数(BPP)和压缩比(CR)是新冠病毒的初始治疗，而处理阶段使用网络训练Convolutional Neural Networks(CNN)与Discrete Second Chebyshev Wavelets Convolutional Neural Network(DSCWCNN)和Discrete Third Chebyshev Wavelets Convolutional Neural Network(DTCWCNN)创建高效的人脸识别算法，并且最好的结果是在精度和最少的时间内实现。使用了制作或实现的彩色图像的两个样本。提出的理论以快速和良好的结果得出，结果在以下表格中显而易见。
URL
https://arxiv.org/abs/2303.13158
