Yann Frauel

Resistance of the double random phase encryption against various attacks

Several attacks are proposed against the double random phase encryption scheme. These attacks are demonstrated on computer-generated ciphered images. The scheme is shown to be resistant against brute force attacks but susceptible to chosen and known plaintext attacks. In particular, we describe a technique to recover the exact keys with only two known plain images. We compare this technique to other attacks proposed in the literature.

Real-time three-dimensional object reconstruction by use of a phase-encoded digital hologram.

A three-dimensional (3D) object reconstruction technique that uses only phase information of a phase-shifting digital hologram and a phase-only spatial-light modulator is proposed. It is well known that a digital hologram can store both amplitude and phase information of an optical electric field and can reconstruct the original 3D object in a computer. We demonstrate that it is possible to reconstruct optically 3D objects using only phase information of the optical field calculated from phase-shifting digital holograms. The use of phase-only information enables us to reduce the amount of data in the digital hologram and reconstruct optically the 3D objects using a liquid-crystal spatial light modulator without optical power loss. Numerical evaluation of the reconstructed 3D object is presented.

Three-Dimensional Imaging and Processing Using Computational Holographic Imaging

Digital holography is a technique that permits digital capture of holograms and subsequent processing on a digital computer. This paper reviews various applications of this technique. The presented applications cover three-dimensional (3-D) imaging as well as several associated problems. For the case of 3-D imaging, optical and digital methods to reconstruct and visualize the recorded objects are described. In addition, techniques to compress and encrypt 3-D information in the form of digital holograms are presented. Lastly, 3-D pattern recognition applications of digital holography are discussed. The described techniques constitute a comprehensive approach to 3-D imaging and processing.

Compression of digital holograms for three-dimensional object reconstruction and recognition

We present the results of applying lossless and lossy data compression to a three-dimensional object reconstruction and recognition technique based on phase-shift digital holography. We find that the best lossless (Lempel-Ziv, Lempel-Ziv-Welch, Huffman, Burrows-Wheeler) compression rates can be expected when the digital hologram is stored in an intermediate coding of separate data streams for real and imaginary components. The lossy techniques are based on subsampling, quantization, and discrete Fourier transformation. For various degrees of speckle reduction, we quantify the number of Fourier coefficients that can be removed from the hologram domain, and the lowest level of quantization achievable, without incurring significant loss in correlation performance or significant error in the reconstructed object domain.

Digital three-dimensional image correlation by use of computer-reconstructed integral imaging

We use integral images of a three-dimensional (3D) scene to estimate the longitudinal depth of multiple objects present in the scene. With this information, we digitally reconstruct the objects in three dimensions and compute 3D correlations of input objects. We investigate the use of nonlinear techniques for 3D correlations. We present experimental results for 3D reconstruction and correlation of 3D objects. We demonstrate that it is possible to perform 3D segmentation of 3D objects in a scene. We finally present experiments to demonstrate that the 3D correlation is more discriminant than the two-dimensional correlation.

A robust Graph Transformation Matching for non-rigid registration

In this paper, we propose a simple and highly robust point-matching method named Graph Transformation Matching (GTM) relying on finding a consensus nearest-neighbour graph emerging from candidate matches. The method iteratively eliminates dubious matches in order to obtain the consensus graph. The proposed technique is compared against both the Softassign algorithm and a combination of RANSAC and epipolar constraint. Among these three techniques, GTM demonstrates to yield the best results in terms of elimination of outliers. The algorithm is shown to be able to deal with difficult cases such as duplication of patterns and non-rigid deformations of objects. An execution time comparison is also presented, where GTM shows to be also superior to RANSAC for high outlier rates. In order to improve the performance of GTM for lower outlier rates, we present an optimised version of the algorithm. Lastly, GTM is successfully applied in the context of constructing mosaics of retinal images, where feature points are extracted from properly segmented binary images. Similarly, the proposed method could be applied to a number of other important applications.

Reduction of speckle in digital holography by discrete Fourier filtering

We present a digital signal processing technique that reduces the speckle content in reconstructed digital holograms. The method is based on sequential sampling of the discrete Fourier transform of the reconstructed image field. Speckle reduction is achieved at the expense of a reduced intensity and resolution, but this trade-off is shown to be greatly superior to that imposed by the traditional mean and median filtering techniques. In particular, we show that the speckle can be reduced by half with no loss of resolution (according to standard definitions of both metrics).

Distortion-tolerant three-dimensional object recognition with digital holography

We present a technique to implement three-dimensional (3-D) object recognition based on phase-shift digital holography. We use a nonlinear composite correlation filter to achieve distortion tolerance. We take advantage of the properties of holograms to make the composite filter by using one single hologram. Experiments are presented to illustrate the recognition of a 3-D object in the presence of out-of-plane rotation and longitudinal shift along the z axis.

Integral imaging with large depth of field using an asymmetric phase mask

We propose to improve the depth of field of Integral Imaging systems by combining an array of phase masks with the traditional lenslet array. We show that obtained elemental images are sharp over a larger range than with a regular lenslet array. We further increase the quality of elemental images by a digital restauration. Computer simulations of pickup and reconstruction are presented.

Neural network for three-dimensional object recognition based on digital holography.

We present a two-layer neural network for processing of three-dimensional (3D) images that are obtained by digital holography. The network is trained with a real 3D object to compute the weights of the layers. Experiments are presented to illustrate the system performance. The system is designed to detect a 3D object in the presence of various distortions. As an example, experiments are presented to illustrate how the system is able to recognize a 3D object with 360 degrees out-of-plane rotation.