Improvement of Color Image Analysis Using a New Hybrid Face Recognition Algorithm based on Discrete Wavelets and Chebyshev Polynomials

• URL: http://arxiv.org/abs/2303.13158v1
• Date: Thu, 23 Mar 2023 10:20:19 GMT
• Title: Improvement of Color Image Analysis Using a New Hybrid Face Recognition Algorithm based on Discrete Wavelets and Chebyshev Polynomials
• Authors: Hassan Mohamed Muhi-Aldeen, Maha Ammar Mustafa, Asma A. Abdulrahman, Jabbar Abed Eleiwy, Fouad S. Tahir and Yurii Khlaponin
• Abstract summary: This work is unique in the use of discrete wavelets that were built from or derived from Chebyshevvolutionals of the second and third kind. The filter Discrete Third Chebyshev Wavelets Transform (FDTCWT) is used in the process of analyzing color images. The best results were achieved in accuracy and in the least amount of time.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• SING: Semantic Image Communications using Null-Space and INN-Guided Diffusion Models [52.40011613324083]
  Joint source-channel coding systems (DeepJSCC) have recently demonstrated remarkable performance in wireless image transmission. Existing methods focus on minimizing distortion between the transmitted image and the reconstructed version at the receiver, often overlooking perceptual quality. We propose SING, a novel framework that formulates the recovery of high-quality images from corrupted reconstructions as an inverse problem.
  arXiv  Detail & Related papers  (2025-03-16T12:32:11Z)
• Multispectral Texture Synthesis using RGB Convolutional Neural Networks [2.3213238782019316]
  State-of-the-art RGB texture synthesis algorithms rely on style distances that are computed through statistics of deep features. We propose two solutions to extend these methods to multispectral imaging.
  arXiv  Detail & Related papers  (2024-10-21T13:49:54Z)
• Enhanced Wavelet Scattering Network for image inpainting detection [0.0]
  This paper proposes several innovative ideas for detecting inpainting forgeries based on low level noise analysis. It combines Dual-Tree Complex Wavelet Transform (DT-CWT) for feature extraction with convolutional neural networks (CNN) for forged area detection and localization. Our approach was benchmarked against state-of-the-art methods and demonstrated superior performance over all cited alternatives.
  arXiv  Detail & Related papers  (2024-09-25T15:27:05Z)
• Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1 [51.404644401997736]
  We propose a complete framework to remove speckle in polarimetric SAR images using a convolutional neural network. Experiments show that the proposed approach offers exceptional results in both speckle reduction and resolution preservation.
  arXiv  Detail & Related papers  (2024-08-28T10:07:17Z)
• WiNet: Wavelet-based Incremental Learning for Efficient Medical Image Registration [68.25711405944239]
  Deep image registration has demonstrated exceptional accuracy and fast inference. Recent advances have adopted either multiple cascades or pyramid architectures to estimate dense deformation fields in a coarse-to-fine manner. We introduce a model-driven WiNet that incrementally estimates scale-wise wavelet coefficients for the displacement/velocity field across various scales.
  arXiv  Detail & Related papers  (2024-07-18T11:51:01Z)
• Wavelet-based Bi-dimensional Aggregation Network for SAR Image Change Detection [53.842568573251214]
  Experimental results on three SAR datasets demonstrate that our WBANet significantly outperforms contemporary state-of-the-art methods. Our WBANet achieves 98.33%, 96.65%, and 96.62% of percentage of correct classification (PCC) on the respective datasets.
  arXiv  Detail & Related papers  (2024-07-18T04:36:10Z)
• Deep Convolutional Framelet Denoising for Panoramic by Mixed Wavelet Integration [0.0]
  One of the most critical challenges in this area has consistently been lowering the image noise. This paper suggests integrating the waveform with the Daubechies (D4) wavelet due to its higher energy concentration and employs the u-Net neural network architecture. The effectiveness of a one-wave network has increased from 0.5% to 1.2%, according to studies done on other datasets.
  arXiv  Detail & Related papers  (2023-01-25T11:00:32Z)
• Multi-stage image denoising with the wavelet transform [125.2251438120701]
  Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information. We propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i.e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and residual block (RB)
  arXiv  Detail & Related papers  (2022-09-26T03:28:23Z)
• Unsupervised Denoising of Optical Coherence Tomography Images with Dual_Merged CycleWGAN [3.3909577600092122]
  We propose a new Cycle-Consistent Generative Adversarial Nets called Dual-Merged Cycle-WGAN for retinal OCT image denoiseing. Our model consists of two Cycle-GAN networks with imporved generator, descriminator and wasserstein loss to achieve good training stability and better performance.
  arXiv  Detail & Related papers  (2022-05-02T07:38:19Z)
• Influence of image noise on crack detection performance of deep convolutional neural networks [0.0]
  Much research has been conducted on classifying cracks from image data using deep convolutional neural networks. This paper will investigate the influence of image noise on network accuracy. AlexNet was selected as the most efficient model based on the proposed index.
  arXiv  Detail & Related papers  (2021-11-03T09:08:54Z)
• Progressive Training of Multi-level Wavelet Residual Networks for Image Denoising [80.10533234415237]
  This paper presents a multi-level wavelet residual network (MWRN) architecture as well as a progressive training scheme to improve image denoising performance. Experiments on both synthetic and real-world noisy images show that our PT-MWRN performs favorably against the state-of-the-art denoising methods.
  arXiv  Detail & Related papers  (2020-10-23T14:14:00Z)
• Unrolling of Deep Graph Total Variation for Image Denoising [106.93258903150702]
  In this paper, we combine classical graph signal filtering with deep feature learning into a competitive hybrid design. We employ interpretable analytical low-pass graph filters and employ 80% fewer network parameters than state-of-the-art DL denoising scheme DnCNN.
  arXiv  Detail & Related papers  (2020-10-21T20:04:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.