<p>In this work, it was proposed to compress the color image after de-noise by proposing a coding for the discrete transport of new wavelets called discrete chebysheve wavelet transduction (DCHWT) and linking it to a neural network that relies on the convolutional neural network to compress the color image. The aim of this work is to find an effective method for face recognition, which is to raise the noise and compress the image in convolutional neural networks to remove the noise that caused the image while it was being transmitted in the communication network. The work results of the algorithm were calculated by calculating the peak signal to noise ratio (PSNR), mean square error (MSE), compression ratio (CR) and bit-per-pixel (BPP) of the compressed image after a color image (256×256) was entered to demonstrate the quality and efficiency of the proposed algorithm in this work. The result obtained by using a convolutional neural network with new wavelets is to provide a better CR with the ratio of PSNR to be a high value that increases the high-quality ratio of the compressed image to be ready for face recognition.</p>
Recently, face recognition system (FRS) is implemented in different applications including a range of vital services like airports and banking systems for security purposes. Therefore, deployed surveillance systems have been established which led to the urgent need to develop a vital face recognition system. In this work, a new algorithm was proposed for recognition of the face, personal and color images by training the convolutional neural network using the MATLAB program to build a new program for detection of the face, then building a separate program to discover the lips, nose, and eyes, New methods were explored to analyze the main and independent components to improve face detection, which is considered one of the important techniques in this work using neural networks and implementation through the MATLAB program.
<span>In this work, new discrete wavelets were derived Hermite polynomials for obtained discrete hermite wavelet transformation (DHWT), and their efficiency for use in image processing is demonstrated by proving the realization of important theorems. Moreover, the role of the new and proposed waveforms in their effective effect in placing the watermark with the color image is clarified, and a program was created using MATLAB software by creating a subprogram for constructing the new wavelet and proving its efficiency with an analytic image. The process is repeated using DHWT to analyze the image. The color image has been subjected to various attacks after which the watermark is retrieved from the image after comparing it with the proposed algorithm and it has proven its power faster and better than the previously suggested methods. The final conclusion shows that using new wavelets DHWT better peak signal of noise ratio (PSNR)s can be obtained and that the proposed algorithm fills in better the lack of awareness of the watermark and its strength under different attacks.</span>
Watermarking in protecting brands from possible counterfeiting is considered one of the methods of processing digital and color images. This technique requires the discovery of advanced methods to obtain the best results by discovering a new filter instead of the standard filters that were used without reaching the peak of the required and optimal values through which the images are analyzed. Colored with the proposed watermark in the process of immersion and separation the new filter is derived from Laguerre polynomials and using mathematical methods to derive the filter. And using it in analyzing the image to reach the best results, and through the proposed New Discrete Laguerre Wavelets Transform NDLWT for watermark algorithms it becomes clear the efficiency of the proposed theory in reading the most important criteria for the quality of the extracted image.
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