Wavelets and LPG-PCA for Image Denoising

In this chapter, a new image denoising approach is proposed. It combines two image denoising techniques. The first one is based on a wavelet transform (WT), and the second one is a two-stage image denoising by PCA (principal component analysis) with LPG (local pixel grouping). In this proposed approach, we first apply the first technique to the noisy image in order to obtain the first estimation version of the clean image. Then, we estimate the noise-level from the noisy image. This estimate is obtained by applying the third technique of noise estimation from noisy images. The third step of the proposed approach consists in using the first estimation of the clean image, the noisy image, and the estimate of the noise-level as inputs of the second image denoising system (LPG-PCA). A comparative study of the proposed technique and the two others denoising technique (one is based on WT and and the second is based on LPG-PCA), is performed. This comparative study used a number of noisy images, and the obtained results from PSNR (peak signal-to-noise ratio) and SSIM (structural similarity) computations show that the proposed approach outperforms the two other denoising approaches (the first one is based on WT and the second one is based on LPG-PCA)

Watermarking for the Secure Transmission of the Key into an Encrypted Image

Ensuring the confidentiality of any data exchanged always presents a great concern for all communication instances. Technically, encryption is the ideal solution for this task. However, this process must deal with the progress of the cryptanalysis that aims to disclose the information exchanged. The risk increases due to the need for a dual transmission that includes the encrypted medium and the decryption key. In a context of chaotic encryption of images, we propose to insert the decryption key into the encrypted image using image watermarking. Thus, only the watermarked encrypted image will be transmitted. Upon reception, the recipient extracts the key and decrypts the image. The cryptosystem proposed is based on an encryption using a dynamic Look-Up Table issued from a chaotic generator. The obtained results prove the efficiency of our method to ensure a secure exchange of images and keys

Semi-supervised auto-encoder for facial attributes recognition

The particularity of our faces encourages many researchers to exploit their features in different domains such as user identification, behaviour analysis, computer technology, security, and psychology. In this paper, we present a method for facial attributes analysis. The work addressed to analyse facial images and extract features in the purpose to recognize demographic attributes: age, gender, and ethnicity (AGE). In this work, we exploited the robustness of deep learning (DL) using an updating version of autoencoders called the deep sparse autoencoder (DSAE). In this work we used a new architecture of DSAE by adding the supervision to the classic model and we control the overfitting problem by regularizing the model. The pass from DSAE to the semi-supervised autoencoder (DSSAE) facilitates the supervision process and achieves an excellent performance to extract features. In this work we focused to estimate AGE jointly. The experiment results show that DSSAE is created to recognize facial features with high precision. The whole system achieves good performance and important rates in AGE using the MORPH II databas

Smart Trust Management for Vehicular Networks

Spontaneous networks such as VANET are in general deployed in an open and thus easily accessible environment. Therefore, they are vulnerable to attacks. Trust management is one of a set of security solutions dedicated to this type of networks. Moreover, the strong mobility of the nodes (in the case of VANET) makes the establishment of a trust management system complex. In this paper, we present a concept of ‘Active Vehicle’ which means an autonomous vehicle that is able to make decision about trustworthiness of alert messages transmitted about road accidents. The behavior of an “Active Vehicle” is modeled using Petri Nets

Credal Fusion of Classifications for Noisy and Uncertain Data

This paper reports on an investigation in classification technique employed to classify noised and uncertain data. However, classification is not an easy task. It is a significant challenge to discover knowledge from uncertain data. In fact, we can find many problems. More time we don't have a good or a big learning database for supervised classification. Also, when training data contains noise or missing values, classification accuracy will be affected dramatically. So to extract groups from  data is not easy to do. They are overlapped and not very separated from each other. Another problem which can be cited here is the uncertainty due to measuring devices. Consequentially classification model is not so robust and strong to classify new objects. In this work, we present a novel classification algorithm to cover these problems. We materialize our main idea by using belief function theory to do combination between classification and clustering. This theory treats very well imprecision and uncertainty linked to classification. Experimental results show that our approach has ability to significantly improve the quality of classification of generic database

Automatic image annotation system using deep learning method to analyse ambiguous images

Image annotation has gotten a lot of attention recently because of how quickly picture data has expanded. Together with image analysis and interpretation, image annotation, which may semantically describe images, has a variety of uses in allied industries including urban planning engineering. Even without big data and image identification technologies, it is challenging to manually analyze a diverse variety of photos. The improvements to the Automated Image Annotation (AIA) label system have been the subject of several scholarly research. The authors will discuss how to use image databases and the AIA system in this essay. The proposed method extracts image features from photos using an improved VGG-19, and then uses nearby features to automatically forecast picture labels. The proposed study accounts for both correlations between labels and images as well as correlations within images. The number of labels is also estimated using a label quantity prediction (LQP) model, which improves label prediction precision. The suggested method addresses automatic annotation methodologies for pixel-level images of unusual things while incorporating supervisory information via interactive spherical skins. The genuine things that were converted into metadata and identified as being connected to pre-existing categories were categorized by the authors using a deep learning approach called a conventional neural network (CNN) - supervised. Certain object monitoring systems strive for a high item detection rate (true-positive), followed by a low availability rate (false-positive). The authors created a KD-tree based on k-nearest neighbors (KNN) to speed up annotating. In order to take into account for the collected image backdrop. The proposed method transforms the conventional two-class object detection problem into a multi-class classification problem, breaking the separated and identical distribution estimations on machine learning methodologies. It is also simple to use because it only requires pixel information and ignores any other supporting elements from various color schemes. The following factors are taken into consideration while comparing the five different AIA approaches: main idea, significant contribution, computational framework, computing speed, and annotation accuracy. A set of publicly accessible photos that serve as standards for assessing AIA methods is also provided, along with a brief description of the four common assessment signs

Speech Enhancement Based on LWT and Artificial Neural Network and Using MMSE Estimate of Spectral Amplitude

In this chapter, we will detail a new speech enhancement technique based on Lifting Wavelet Transform (LWT) and Artifitial Neural Network (ANN). This technique also uses the MMSE Estimate of Spectral Amplitude. It consists at the first step in applying the LWTto the noisy speech signal in order to obtain two noisy details coefficients, cD1 and cD2 and one approximation coefficient, cA2. After that, cD1 and cD2 are denoised by soft thresholding and for their thresholding, we need to use suitable thresholds, thrj,1≤j≤2. Those thresholds, thrj,1≤j≤2, are determined by using an Artificial Neural Network (ANN). The soft thresholding of those coefficients, cD1 and cD2, is performed in order to obtain two denoised coefficients, cDd1 and cDd2 . Then the denoising technique based on MMSE Estimate of Spectral Amplitude is applied to the noisy approximation cA2 in order to obtain a denoised coefficient, cAd2. Finally, the enhanced speech signal is obtained from the application of the inverse of LWT, LWT−1 to cDd1, cDd2 and cAd2. The performance of the proposed speech enhancement technique is justified by the computations of the Signal to Noise Ratio (SNR), Segmental SNR (SSNR) and Perceptual Evaluation of Speech Quality (PESQ)