Abstract
Large communications of voice and images over the Internet, which leads to limited space for very large data during the aforementioned correspondence, to overcome this issue to maintain the quality of this technology.
The proposal in the efficient algorithm in this work is a method to derive the two new filters from the second and third Chebyshev polynomials by forming the discrete wavelets with the mother wavelet to be used in image processing in order to overcome the problem mentioned above due to the correspondence, The filters that were derived are Filter Discrete Second Chebyshev Wavelets Transform (FDSCWT) and Filter Discrete Third Chebyshev Wavelets Transform (FDTCWT)to process the image by analysis, noise reduction, and image compression.
Many of the techniques previously used in the field of image processing do not preserve image information during processing, but when using the new technology proposed in this work, it has been proven to preserve the image with its important information and data through the readings obtained shown in the tables below. These readings are average. Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), Bits Per Pixel (BPP), and Compression Ratio (CR) in preprocessing.
After the initial processing stage, the deep learning stage begins in the field of artificial intelligence. A (CNN) is trained with the two new filters to be the first Discrete Second Chebyshev Wavelets (DSCWCNN) and the second Discrete Third Chebyshev Wavelets Convolutional Neural Network (DTCWCNN), with the code being generated in the MATLAB program with a network Alex Net to complete the classification process that was added in this work to implement the recognition technology. Faces detection with new filters in deep learning to be a unique experience to reach a high level of accuracy of 98.60 % with the network for the filter DSCWCNN and 98.92 % with the network for the filter DTCWCNN in a very short time, which will be mentioned later in the work