Tailored dual polarization encryption-coherence modulation-based decryption scheme for a predefined uniformly distributed noisy output image.

In this paper, we propose a tailored dual polarization encryption scheme (T-DPES) based on a polarization encoding of input target images into a same predefined uniformly distributed noisy output image, using Mueller-Stokes's formalism. The optical encryption setup, based on a polarizer and a pixelated polarizer, uses a random image key as a first key and a pixelated polarizer as a second key. This key depends on the input image, the image key and the predefined uniformly distributed noisy output image. The decryption process is carried out, for the first time to our knowledge, optically by combining the coherence modulation to the DPES scheme. The performance evaluation and the crypto-analyze of T-DPES show their strength and robustness against attacks, thus overcoming limitations of previous optical encryption techniques.

A Survey of NOMA for VLC Systems: Research Challenges and Future Trends.

Visible light communication (VLC) has become a promising technology for high data rate communications and an attractive complementary to conventional radio frequency (RF) communication. VLC is a secure, energy efficient and cost-effective technology that exploits the existing infrastructure, particularly in indoor environments, for wireless data transmission. Nevertheless, the main limitation of developing high data rate VLC links is the narrow modulation bandwidth of light-emitting diodes (LEDs), which is in the megahertz range. The power domain nonorthogonal multiple access (PD-NOMA) scheme is envisioned to address several challenges in VLC systems. In this paper, we present a detailed overview of PD-NOMA based VLC systems. Moreover, we introduce insights on some PD-NOMA VLC system constraints and challenges such as power allocation, clipping effect, MIMO and security. Finally, we provide open research problems as well as possible directions for future research to pave the way for the implementation of PD-NOMA VLC systems.

Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment.

In order to improve behavioral analysis systems in urban environments, this paper proposes, using data extracted from video surveillance cameras, a tracking method through two approaches. The first approach consists in comparing the position of people between two images of a video and to perform tracking by proximity. The second method using Kalman filters is based on the anticipation of the position of an individual in the upcoming image. The use of this method proves to be more efficient as it allows continuing a detection even when people cross each other or when they pass behind obstacles. The use of Kalman filters in this domain provides a new approach to obtain reliable tracking and information on speed and trajectory variations. The proposed method is innovative in the way the tracking is performed and the results are exploited. Experiments were conducted in a real situation and showed that the use of some elements of the first method could be reused to integrate a notion of distance in the method based on the Kalman filter and thus improve the latter both in tracking and in detecting of abnormal behavior. This article deals with the functioning of the two methods as well as the results obtained with the same scenarios. The experimentation concludes through concrete results that the Kalman filter method is more efficient than the proximity method alone. A sample result is available online for two of the seven videos used in this article (accessed on 19 July 2021).

Efficient Sky Dehazing by Atmospheric Light Fusion.

In this article, we present a new method of dehazing based on the Koschmieder model, which aims to restore an image that has been affected by haze. The difficulty is to improve the estimation of the transmission and the atmospheric light that generally suffer from the nonhomogeneity and the random variability of the environment. The keypoint is to enhance the dehazing of very bright regions of the image in order to improve the treatment of the sky that is often overestimated or underestimated compared to the rest of the scene. The approach proposed in this paper is based on two main contributions: 1. an L0 gradient optimization function weighted by a set of Gaussian filters and based on an iterative algorithm for optimization convergence. Unlike the existing methods using a single value of the atmospheric light for the whole image, our method uses a set of values neighboring an initial estimated value. The fusion is then applied based on Laplacian and Gaussian pyramids to combine all the relevant information from the set of images constructed from atmospheric lights and improves the contrast to recover the colors of the sky without any artifacts. Finally, the results are validated by three criteria: an autocorrelation score (ZNCC), a similarity measure (SSIM) and a visual criterion. The experiments carried out on two datasets show that our approach allows a better dehazing of the images with higher SSIM and ZNCC measurements but also with better visual quality.

Enhancing Optical Correlation Decision Performance for Face Recognition by Using a Nonparametric Kernel Smoothing Classification.

Optical correlation has a rich history in image recognition applications from a database. In practice, it is simple to implement optically using two lenses or numerically using two Fourier transforms. Even if correlation is a reliable method for image recognition, it may jeopardize decision making according to the location, height, and shape of the correlation peak within the correlation plane. Additionally, correlation is very sensitive to image rotation and scale. To overcome these issues, in this study, we propose a method of nonparametric modelling of the correlation plane. Our method is based on a kernel estimation of the regression function used to classify the individual images in the correlation plane. The basic idea is to improve the decision by taking into consideration the energy shape and distribution in the correlation plane. The method relies on the calculation of the Hausdorff distance between the target correlation plane (of the image to recognize) and the correlation planes obtained from the database (the correlation planes computed from the database images). Our method is tested for a face recognition application using the Pointing Head Pose Image Database (PHPID) database. Overall, the results demonstrate good performances of this method compared to competitive methods in terms of good detection and very low false alarm rates.

Enhancing underwater optical imaging by using a low-pass polarization filter.

Object identification in highly turbid optical media depends mainly on the quality of collected images. Underwater images acquired in a turbid environment are generally of very poor quality. Attenuation and backscattering of light by water, by materials dissolved in the water, and by particulate material are the main causes of the degradation of underwater images. It is therefore essential to improve the quality of such images to facilitate object identification. The focus of this paper is to report the principle and validation of a fast and effective method of improving the quality of underwater images. On the one hand, this method uses a polarimetric imaging optical system to reduce the effect of diffusion on the image acquisition. On the other hand, it is based on an optimized version of the dark channel prior (DCP) method that has received a great deal of attention for image dehazing. Results derived from images obtained in a controlled laboratory water tank environment with different turbidity conditions and images from tests using the proposed method at sea demonstrate an ability to significantly improve visibility and reduce runtime by a factor of about 50 for a 4K image when compared to conventional DCP methods.

Design and simplified calibration of a Mueller imaging polarimeter for material classification.

This study is concerned with the design of a Mueller imaging polarimeter for the visualization of spatially varying Mueller matrix fields. A simplified calibration procedure is advocated, where all the optical elements are calibrated simultaneously rather than independently as in the state of the art. This is shown to significantly reduce the bias inherent to sequential calibration methods. In addition, this procedure requires no reference sample, it allows calibration both in transmission or in reflection modes, and it relies on ready-to-use cameras. Put together, these novelties should help nonspecialists in optics in designing and calibrating a Mueller imaging polarimeter for applications such as material classification.

One lens optical correlation: application to face recognition.

Despite its extensive use, the traditional 4f Vander Lugt Correlator optical setup can be further simplified. We propose a lightweight correlation scheme where the decision is taken in the Fourier plane. For this purpose, the Fourier plane is adapted and used as a decision plane. Then, the offline phase and the decision metric are re-examined in order to keep a reasonable recognition rate. The benefits of the proposed approach are numerous: (1) it overcomes the constraints related to the use of a second lens; (2) the optical correlation setup is simplified; (3) the multiplication with the correlation filter can be done digitally, which offers a higher adaptability according to the application. Moreover, the digital counterpart of the correlation scheme is lightened since with the proposed scheme we get rid of the inverse Fourier transform (IFT) calculation (i.e., decision directly in the Fourier domain without resorting to IFT). To assess the performance of the proposed approach, an insight into digital hardware resources saving is provided. The proposed method involves nearly 100 times fewer arithmetic operators. Moreover, from experimental results in the context of face verification-based correlation, we demonstrate that the proposed scheme provides comparable or better accuracy than the traditional method. One interesting feature of the proposed scheme is that it could greatly outperform the traditional scheme for face identification application in terms of sensitivity to face orientation. The proposed method is found to be digital/optical implementation-friendly, which facilitates its integration on a very broad range of scenarios.

Double random phase encryption scheme to multiplex and simultaneous encode multiple images.

Here we present a new approach of multiplexing and simultaneous encoding of target images. Our approach can enhance the encryption level of a classical double random phase (DRP) encryption system by adding a supplementary security layer. The new approach can be divided into two security layers. The first layer is called the multiplexing level, which consists in using iterative Fourier transformations along with several encryption key images. These latter can be a set of biometric images. At the second layer, we use a classical DRP system. The two layers enable us to encode several target images (multi-encryption) and to reduce, at the same time, the requested decoded information (transmitted or storage information).