Image analysis took wide areas in many fields, including medicine, physics, and other areas where you need a tool to deal with it smoothly and softly without losing the original image information. Using an image of a sample of a physical atom that was analyzed and highlighting the compression and raising the noise, histogram and statistics the image statistics where the best results were recorded when using a specific threshold i.e. when pressing the methods were used the first has the threshold methods is Balance sparsity-norm, Remove near 0 and Bal-sparsity-norm(sqrt). As for the methods of raising the noise are fixed form thresholding method with soft threshold, penalize high with hard threshold, penalize medium with hard threshold, penalize low with hard threshold, Bal sparsity norm (sqrt) with soft threshold, where image parameters were divided into approximation coefficients and details coefficients. Through the analysis, a suitable threshold value was obtained, which helps to restore energy that leads to the fact that the compressed necessity did not lose much of its original information, which proves the new wavelets in the field of physical and medical imaging.
<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.
This article describes a new image processing method in order to enhance the images under testing based on discrete Hermite wavelet filter. Discrete Hermite Wavelet Transform, which was used in image processing, including compression and noise removal was used after a number of theories proved to be mathematically ready for use in image processing. Through Discrete Hermite Wavelet Transform at different levels by finding a new filter and using it to find peak-to-noise ratio values (PSNR), compression ratio, mean square error (MSE) and bits per pixel found. Achieving a high compression ratio is acquired by using a new image decomposition algorithm. Bit reduction per pixel is obtained at the second level when increasing the level of decomposition to obtain compression ratio while PSNR is decreased with the basic wavelets, due to the features that characterize Discrete Hermite Wavelet Transform. A new filter was discovered more efficient and effective in reducing the error significantly in rebuilding, MSE and bits per pixel, the samples image is used show efficient intermittent wavelets that were built in this work. This method enabling to extract the integration matrices using Hermite wavelet operation matrix of integration that leads to improve the quality of images under testing. The obtained results for decreasing of and increasing of confirm the accuracy and effectiveness of the proposed method. These results can be used in many fields such as medicine, science treatment, compression, and noise removal images.
<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>
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