In previous study, a tomographic imaging system is built up for measuring the three-dimensional refractive index distribution inside the micrometer-sized biological cell by optically driven full-angle rotation scheme based on digital holographic microscopy, named as optical-driven tomographic DHM (OT-DHM) system. However, a small perturbation of the system will lead the inaccurate of the positions and the orientation of the micrometer-sized sample, thus the automatic calibration of the reconstructed phase images in the OT-DHM system is required. For this purpose, a novel model-based algorithm is proposed, in which we employ a 3-D ellipse shape for modeling the samples. The parameters of the ellipse-like shape on a small number of the projections are estimated and used them to build up the 3-D ellipse model of the samples. In advance, the reconstructed phase images are highly contaminated by the uneven background and coherence speckle noise. The block-based between-class criterion is used to suppress the effect of the non-uniform background, and the anisotropic diffusion process is utilized for the noise cleaning, including shot noise and speckles noise on the reconstructed phase. The boundary of the cell in each projection can be considered as the 2-D ellipse, and used to estimate the parameters of the 2-D ellipse. The established 3-D ellipse shape is applied for the calibration of the spatial positions and the orientations of the all other rotational angles. With the automatic calibration algorithm, the OT-DHM system can effectively reconstructed the three-dimensional refractive index distribution inside the micrometer-sized samples.
We investigate the practical design of a complex-encoded key mask for optical encryption and decryption based on joint transform correlation architecture. The mask is created by using two coupled liquid-crystal spatial light modulators, one operating in amplitude mode and the other in phase mode. We develop a modified iterative Fourier transformation algorithm to design an optimal complex key mask, which is applied and mapped to the complex modulation of the liquid-crystal devices for optical implementation. The limitations of the devices on the system design are investigated and analyzed. The width constraints of the key mask are also derived, based on the joint transform correlation architecture for optical realization. Experimental results show the decryption performance and the shift-invariance of the complex key mask.
We examine the effects of transmission error on the digital holograms in phase-shifting digital holography. It is evident that the reconstruction object image is vulnerable to transmission errors from the noise channel. An estimation-based reconstruction algorithm is used to detect the error points and the largest-error hologram of the error points. Based on the detection result, the adaptive algorithm reconstructs the digital hologram by removing the noise effect on the largest-error hologram. With accurate detection, the proposed algorithm is able to reconstruct an object image from a digital hologram with severe transmission errors.
This work presents an experimental demonstration of an optical joint transform correlator based on the wavelet subband filter for texture pattern recognition. The optical wavelet subband filter is implemented using 4f filtering architecture and utilized to extract the texture features of fingerprints under noisy environments through frequency- and orientation-selective properties. The filtered texture features with noise reduction are applied to optimize recognition via joint transform correlation. Experimental results show that the optical wavelet subband filter enhances the significant texture features from corrupted fingerprints and increases the pattern discrimination of the joint transform correlator.
We propose and demonstrate an optical watermarking scheme using a digital holographic technique. The holographic watermark is constructed by an off-axis diffuse-type hologram and embedded into a cover image with appropriate weighting. Detection of the hidden mark is optically implemented using a VanderLugt correlator with the watermarked matched filter. Detected correlation is spatially separable and avoids interference from the cover image.
We investigate a new signal model based on the wavelet packet representation to achieve high resolution beam-forming. With a properly selected signal model, we perform a robust iterative algorithm to extrapolate the data outside of the observation interval and, therefore, increase the resolution of the beam patterns for a 1-D linear array. The wavelet packet sign model is particularly attractive in representing nonstationary signals such as the chirp signal. The superior performance of the resulting beamformer is demonstrated by numerical experiments.
Proc. SPIE. 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V
KEYWORDS: Mathematical modeling, Signal to noise ratio, Wavelets, Interference (communication), Linear filtering, Signal processing, Algorithm development, Space operations, Detection theory, Vector spaces
We propose a scale-limited signal model based on wavelet representation and study the reconstructability of scale-limited signals via extrapolation in this research. In analogy with the band-limited case, we define a scale-limited time-concentrated operator, and examine various vector spaces associated with such an operator. It is proved that the scale-limited signal space can be decomposed into the direct sum of two subspaces and only the component in one subspace can be exactly reconstructed, where the reconstructable subspace can be interpreted as a space consisting of scale/time-limited signals. Due to the ill-posedness of scale-limited extrapolation, a regularization process is introduced for noisy data extrapolation.
A new approach for signal extrapolation based on wavelet representation known as scale-time limited extrapolation and a denoising process is investigated in this research. We first examine a new signal modeling technique using wavelets and the corresponding scale-time limited signal extrapolation algorithm. Then, the sensitivity of the algorithm to noise is discussed, and a denoising algorithm based on the time-localization property of the wavelet transform is proposed. By integrating the denoising process and the iterative scale-time limited extrapolation algorithm, we obtain a very robust signal extrapolation algorithm for noisy data. A simulation result of signal extrapolation from noisy observed data is presented to illustrate the performance of the proposed robust signal extrapolation algorithm.