Optical security system using multiple-reference joint transform correlation

Binary phase only filters for rotation and scale invariant pattern recognition with the joint transform correlator

A novel phase-encoded amplitude-modulated joint transform correlator (PAJTC) technique is presented, which yields superior output correlation signals and better noise robustness than a classical JTC. In the proposed PAJTC technique, firstly, the input targets are encoded with an application of a phase mask, and then the joint power spectrum (JPS) is multiplied by an amplitude-modulated filter (AMF) of the reference object before the inverse Fourier transforming. An enhanced amplitude-modulated filter, in which the modulation factor varies according to the noise presented in the input scenes, is used in the PAJTC technique. The PAJTC technique eliminates extraneous signals especially for the JTC with multiple input targets. An optoelectronic implementation schematic diagram for the PAJTC technique and computer simulations of the performances of the classical JTC and the PAJTC for comparison are presented.

We investigate a binary encoding technique to represent grayscale nonlinear joint transform correlators. We show that the joint power spectrum can be binary encoded to represent gray-scale nonlinear joint transform correlators. The threshold function is computed analytically in terms of the power spectra of the input signal and the reference signal. Computer simulations of the correlation tests for various degrees of nonlinear transformation of the joint power spectrum are presented.

Angle gathers are important for true-amplitude migration, migration velocity analysis, and angle-dependent inversion. Among existing methods, calculating the [Formula: see text] direction vector is efficient, but it can give only one direction per grid point and fails to give multiple directions for overlapping wavefields associated with multipaths and reflections. The slowness vectors (SVs) in [Formula: see text] and [Formula: see text] can be connected by Fourier transforms (FTs); the forward FT from [Formula: see text] to [Formula: see text] decomposes the wavefields into different vector components, and the inverse FT sums these components into a unique direction. Therefore, the SV has multiple directions in [Formula: see text], but it has only one direction in [Formula: see text]. Based on this relation, we have separated the computation of propagation direction into two steps: First, we used the forward FT, [Formula: see text] binning, and several inverse FTs to separate the wavefields into vector subsets with different approximate propagation angles, which contained much less wave overlapping; then, we computed [Formula: see text] SVs for each separated wavefield, and the set of these single-direction SVs constituted a multidirectional SV (MSV). In this process, the FTs between [Formula: see text] and [Formula: see text] domains required a large input/output (I/O) time. We prove the conjugate relation between the decomposition results using positive- and negative-frequency wavefields, and we use complex-valued modeling to obtain the positive-frequency wavefields. Thus, we did wavefield decomposition in [Formula: see text] instead of [Formula: see text], and avoided the huge I/O caused by the FT between the [Formula: see text] and [Formula: see text] domains. Our tests demonstrated that the MSV can give multiple directions for overlapping wavefields and improve the quality of angle gathers.

We describe a binary joint transform correlator (JTC) that uses a synthetic discriminant function (SDF) at the input plane to store the reference images. The SDF is displayed at the input plane side by side the input scene. The input scene, the SDF, and their joint power spectrum are thresholded to only two values. Thus, binary spatial light modulators (SLMs) can be used to implement the correlator. The correlation performance of the SDF-based binary JTC is compared to that of the classical SDF-based correlator in the areas of correlation peak intensity, peak to sidelobe ratio, signal-to-noise ratio, and correlation width. We show that the SDF-based binary JTC outperforms the classical correlator in all these areas. Furthermore, a single binary SLM can be used to read in sequentially the binarized input signals and the binarized joint power spectrum which results in a significant reduction in cost, size, and complexity of the system.

Practical aspects of Fourier transform and correlation based processing of spectrochemical data

A no interference encryption and decryption for a digital image is presented in this paper. The encrypting process consists of a fractional Fourier transformation and a Fourier transformation. A digital image is first coded with a random phase plate, then it takes a fractional Fourier transformation. The transformed field function is expanded 4 times and coded with another random phase mask. Meanwhile the system takes a Fourier transformation. The real part of the transformed function is taken as an encrypted image. For the decryption of the digital image, first the encrypted image undergoes an inverse Fourier transformation. Then the upper left corner of the transformed function is cut off. At last the cut-off function takes an inverse fractional Fourier transformation. The original digital image can be extracted from the final transformed function. Compared with optical holography, an interference process is not needed in our program. This will reduce the complexity of optical system.Keywordsoptical image securityFourier transformationfractional Fourier transformation

The technique of the multiple phase encoding for optical security and verification systems is presented in this paper. This technique is based on a 4-f optical correlator that is a common architecture for optical image encryption and verification systems. However, two or more phase masks are iteratively retrieved by use of the proposed multiple phases retrieval algorithm (MPRA) to obtain the target image. The convergent speed of the iteration process in the MPRA is significantly increased and the recovered image is much more similar to the target image than those in previous approaches. In addition, the quantization effects due to the finite resolution of the phase levels in practical implementation are discussed. The relationships between the number of phase masks and the quantized phase levels are also investigated. According to the simulation results, two and three phase masks are enough to design an efficient security verification system with 64 and 32 phase levels, respectively.

A new optical security system for image encryption based on a nonlinear joint transform correlator (JTC) and Fresnel transform is proposed. A lensless optical encryption system, which is a simplified version of previous JTCbased encryption systems, produces a complex distribution in the Fresnel domain, whose intensity is captured by a power-law sensor and nonlinearly modified to yield the real-valued encrypted image. The nonlinearity plays an essential role in the decryption system which, in turn, contains an optical fractional Fourier transform to retrieve the primary image. The security system proposed in this work is a novel extension of the conventional JTC-based encryption system from the Fourier domain to the Fresnel domain. Additional advantages of the proposed system are: simplification of the optical setup, alleviated alignment requirements, and an additional key (the propagation distance) that improves security. Numerical simulations verify the validity of this new optical security system.

In this paper we propose a hybrid representation of environment models by a point representation augmented to geometric representation of surface polygons reconstructed from multiple reference images, through which a real time walkthrough of a complex environment can be achieved. By the method, we start from classification of pixels of the source images into two categories, corresponding respectively to the planar and non-planar surfaces in 3D space. For the pixels corresponding to the planar surfaces, the plane coefficients are reconstructed and all their appearances in the reference images are merged to form uniformly sampled texture images by a comparison of sampling rate and resampling. For the pixels corresponding to the non-planar surfaces, a point representation is applied and the redundant pixels are removed again through sampling rate comparison. The remained pixels are organized by OBB-tree according to their space coordinates. At the same time, the holes that are unable to be captured by all the reference images are pre-filled in the preprocessing phase so that the probability of hole appearance in walkthrough is greatly reduced. Under this hybrid representation, texture mapping and point warping are employed to render the novel views, to take full advantages of the acceleration utility of graphics hardware.

The ability to accurately predict natural backgrounds over a wide range of potential image acquisition conditions is key to wide-area change detection. The lack of robustness in pure image-based techniques has led to the development of site modeling and model-supported exploitation approaches. Instead of using multiple images to build a site model, we consider the possibility of using them directly to estimate the background of a new image for change detection. Our method uses a nonlinear mean-squared estimation technique to compute image backgrounds from multiple reference images. In comparing linear and nonlinear estimators, we find that although the performance of both improves as the number of reference images increases, the nonlinear estimator yields significantly better background estimates and is much less sensitive to registration errors between images than the linear estimator. We show that as the number of references increases, the pixels within a region become physically significant; i.e., they correspond to parts of the scene with similar physical properties. Synthetic aperture radar (SAR) background modeling and change detection results including a crosssensor change detection example in which SAR reference images are used to predict the background of an electro-optical (EO) image are presented. Significant performance gains over linear change detection (up to two orders of magnitude reduction in the false alarm rate) are demonstrated in each case over a 60-deg range of SAR sensor aspect angles.

Removing rain streaks from a single image presents a significant challenge due to the spatial variability of the streaks within the rainy image. While data-driven rain removal algorithms have shown promising results, they remain constrained by issues such as heavy reliance on large datasets and limited interpretability. In this paper, we propose a novel approach for single-image de-raining that is guided by Fourier Transform prior knowledge. Our method utilises inherent frequency domain information to efficiently reduce rain streaks and restore image clarity. Initially, the rainy image is decomposed into its amplitude and phase components using the Fourier Transform, where rain streaks predominantly affect the amplitude component. Following this, data-driven algorithms are employed separately to process the amplitude and phase components. Enhanced features are then reconstructed using the inverse Fourier Transform, resulting in improved clarity. Finally, a multi-scale neural network incorporating attention mechanisms at different scales is applied to further refine the processed features, enhancing the robustness of the algorithm. Experimental results demonstrate that our proposed method significantly outperforms existing state-of-the-art approaches, both in qualitative and quantitative evaluations. This innovative strategy effectively combines the strengths of Fourier Transform and data-driven techniques, offering a more interpretable and efficient solution for single-image de-raining (Code: https://github.com/zhengchaobing/DeRain).

A real-valued image encryption by an optical system of a Fresnel diffraction and a Fourier transform

The increasing need for optical security systems can be perfectly illustrated by reviewing the developments in security printing. The invention of photography in the 19th century forced the production of securities to introduce colour. Colour photography was encountered by the introduction of fine line structures, etc. The hate-love relationship between security printing and optical security systems has been beneficial for both of them in the past. With the invention of lasers and the application of the laser technology in the graphic arts, a merger of security printing and optical security systems has taken place.

Banking security systems are emerging as a matter of great importance nowadays. We are presenting here a smart security system, which has been designed to protect the bank vault from being theft or unauthorized access. This highly secured system is based on Infrared ray, GSM technology, different types of sensors (Motion sensor, Laser sensor, Sound sensor and Gas sensor), IP camera, microcontroller, keyboard, and LCD. GSM technology will be used here to send any type of warning message to the concerned phone number. According to our concept a biometric function, i.e., hand geometry, an iris scanner, a fingerprint scanner or heartbeat rate with a password/PIN can be used here which will be responsible for the fine security of the principle gate of the vault. It may also include Infrared radiation between the walls of bank vault room to provide increased security of the vault. The walls of the vault room is specially designed in two sections maintaining collateral gap where the Infrared radiation will pass from the Infrared radiation sources to prevent any accident like wall break robbery. The proposed security system of the bank vault has been implemented as well as tested several times and found more effective than the other existing security systems.