Resolution enhancement of holographic printer using a hogel overlapping method.

We propose a hogel overlapping method for the holographic printer to enhance the lateral resolution of holographic stereograms. The hogel size is directly related to the lateral resolution of the holographic stereogram. Our analysis by computer simulation shows that there is a limit to decreasing the hogel size while printing holographic stereograms. Instead of reducing the size of hogel, the lateral resolution of holographic stereograms can be enhanced by printing overlapped hogels, which makes it possible to take advantage of multiplexing property of the volume hologram. We built a holographic printer, and recorded two holographic stereograms using the conventional and proposed overlapping methods. The images and movies of the holographic stereograms experimentally captured were compared between the conventional and proposed methods. The experimental results confirm that the proposed hogel overlapping method improves the lateral resolution of holographic stereograms compared to the conventional holographic printing method.

An autocorrelator based on a Fabry-Perot interferometer.

An autocorrelator based on a Fabry-Perot interferometer is proposed for ultrashort pulse measurement. Main features of this autocorrelator due to the superposition of multiple pulses were investigated experimentally and theoretically. It turns out that the signal from a Fabry-Perot interferometer can be used as an autocorrelator signal. This autocorrelator provides more compact setup with a much easier alignment than a conventional autocorrelator based on a Michelson interferometer.

A robust hidden Markov Gauss mixture vector quantizer for a noisy source.

Noise is ubiquitous in real life and changes image acquisition, communication, and processing characteristics in an uncontrolled manner. Gaussian noise and Salt and Pepper noise, in particular, are prevalent in noisy communication channels, camera and scanner sensors, and medical MRI images. It is not unusual for highly sophisticated image processing algorithms developed for clean images to malfunction when used on noisy images. For example, hidden Markov Gauss mixture models (HMGMM) have been shown to perform well in image segmentation applications, but they are quite sensitive to image noise. We propose a modified HMGMM procedure specifically designed to improve performance in the presence of noise. The key feature of the proposed procedure is the adjustment of covariance matrices in Gauss mixture vector quantizer codebooks to minimize an overall minimum discrimination information distortion (MDI). In adjusting covariance matrices, we expand or shrink their elements based on the noisy image. While most results reported in the literature assume a particular noise type, we propose a framework without assuming particular noise characteristics. Without denoising the corrupted source, we apply our method directly to the segmentation of noisy sources. We apply the proposed procedure to the segmentation of aerial images with Salt and Pepper noise and with independent Gaussian noise, and we compare our results with those of the median filter restoration method and the blind deconvolution-based method, respectively. We show that our procedure has better performance than image restoration-based techniques and closely matches to the performance of HMGMM for clean images in terms of both visual segmentation results and error rate.

Image segmentation using hidden Markov Gauss mixture models.

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. We develop a multiclass image segmentation method using hidden Markov Gauss mixture models (HMGMMs) and provide examples of segmentation of aerial images and textures. HMGMMs incorporate supervised learning, fitting the observation probability distribution given each class by a Gauss mixture estimated using vector quantization with a minimum discrimination information (MDI) distortion. We formulate the image segmentation problem using a maximum a posteriori criteria and find the hidden states that maximize the posterior density given the observation. We estimate both the hidden Markov parameter and hidden states using a stochastic expectation-maximization algorithm. Our results demonstrate that HMGMM provides better classification in terms of Bayes risk and spatial homogeneity of the classified objects than do several popular methods, including classification and regression trees, learning vector quantization, causal hidden Markov models (HMMs), and multiresolution HMMs. The computational load of HMGMM is similar to that of the causal HMM.