Superresolution in color videos acquired through turbulent media.

Color videos acquired with a single CCD through turbulent media can be enhanced in their resolution beyond the limit defined by the image sampling rate. We provide a mathematical justification for this claim, present an efficient superresolution algorithm and its experimental verification on a real-life video, and finally, discuss its potentials and limitations.

Redundancy of stereoscopic images: Experimental evaluation.

With the recent advancement in visualization devices over the last years, we are seeing a growing market for stereoscopic content. In order to convey 3D content by means of stereoscopic displays, one needs to transmit and display at least 2 points of view of the video content. This has profound implications on the resources required to transmit the content, as well as demands on the complexity of the visualization system. It is known that stereoscopic images are redundant which may prove useful for compression and may have positive effect on the construction of the visualization device. In this paper we describe an experimental evaluation of data redundancy in color stereoscopic images. In the experiments with computer generated and real life test stereo images, several observers visually tested the stereopsis threshold and accuracy of parallax measurement in anaglyphs and stereograms as functions of the blur degree of one of two stereo images. In addition, we tested the color saturation threshold in one of two stereo images for which full color 3D perception with no visible color degradations was maintained. The experiments support a theoretical estimate that one has to add, to data required to reproduce one of two stereoscopic images, only several percents of that amount of data in order to achieve stereoscopic perception.

Algorithm for reconstruction of digital holograms with adjustable magnification.

A new algorithm that allows for reconstruction of digital holograms with adjustable magnification is proposed. The algorithm involves two reconstruction steps implemented by a conventional single Fourier-transform algorithm. The advantages of the algorithm lie in its adaptability to various object sizes and recording distances as well as in its capability to maintain the pitch of a reconstructed image, independent of the reconstruction distance and wavelength for objects larger than a CCD. The feasibility of the algorithm is demonstrated by experiments. The algorithm is especially useful for reconstructing color holograms and for metrological applications.

Integration in the Fourier domain for restoration of a function from its slope: comparison of four methods.

In some measurement techniques the profile, f(x), of a function should be obtained from the data on measured slope f(?)(x) by integration. The slope is measured in a given set of points, and from these data we should obtain the profile with the highest possible accuracy. Most frequently, the integration is carried out by numerical integration methods [Press et al., Numerical Recipes: The Art of Scientific Computing (Cambridge U. Press, Cambridge, 1987)] that assume different kinds of polynomial approximation of data between sampling points. We propose the integration of the function in the Fourier domain, by which the most-accurate interpolation is automatically carried out. Analysis of the integration methods in the Fourier domain permits us to easily study and compare the methods' behavior.

Input-image homogenization as a method to improve a correlator's discrimination capability.

We propose an input-image preprocessing method consisting of homogenization of the image to improve the discrimination capability of a correlation-based recognition process. This method is an approximation of the optimal filter. It offers the advantage that correlation with the preprocessed images can easily be implemented in an optical correlator working with phase-only spatial light modulators.

Nonlinearity optimization in nonlinear joint transform correlators.

Three types of nonlinear transformations of the joint spectrum in nonlinear joint transform correlators (NLJTC's) are investigated with the purpose of achieving the highest discrimination capability in target location in a cluttered background: logarithmic transformation and the (1/k)th law transformation in combination with the limitation of the signal dynamic range and binarization by thresholding. By computer simulation carried out on a set of test images, it is shown that application of these transformations in NLJTC's may considerably improve the correlator's capacity to locate and recognize properly small objects on a cluttered background, provided there is proper selection of nonlinearity parameters. It is also shown that a moderate blur of the joint spectrum in such NLJTC's before nonlinear transformation is permissible, which simplifies the requirements of correlator optical alignment, the resolution power of correlator electronic components, or both.

Efficient algorithm for discrete sinc interpolation.

A new computing method for discrete-signal sinc interpolation suitable for use in image and signal processing and the synthesis of holograms is described. It is shown to be superior to the commonly used zero-padding interpolation method in terms of interpolation accuracy, flexibility, and computational complexity.

Color pattern recognition with circular component whitening.

Polychromatic object recognition based on circular whitening preprocessing of red-green-blue components and multichannel matched filtering is described. Computer simulations and experimental results are provided to facilitate recognizing a color target among objects of similar shape but with different color contents. Experimental results are obtained with an optical correlator with two spatial light modulators, one to introduce the scene and the second one to introduce the filter.

Optical correlators with (-k)th-law nonlinearity: optimal and suboptimal solutions.

A computer simulation of nonlinear correlators with (-k)th-law nonlinearity has been implemented. The nonlinearity is applied to the input-image power spectrum, with either a matched filter or a phase-only filter representing the reference object. Optimal correlators (with an exactly known power spectrum of the background-image component) and suboptimal correlators have been studied in order to establish potential limits and achievable figures for the correlator's discrimination capability in a target location in a cluttered background. For the suboptimal correlators, different values of the nonlinearity index k have been investigated for two methods of the image's power-spectrum estimation and for different degrees on the limitation of the nonlinearity's dynamic range. The results show that the nonlinear correlators promise significant improvement in the correlator's discrimination capability and provide important information for evaluating the practical aspects of the correlator's design.

Computer-assisted technologies in lung cancer diagnosis and staging.

A paradigm for digital image processing in radiological diagnosis and an appropriate algorithmic instrumentation toolset for the implementation of image processing methods on inexpensive computers and workstations are outlined briefly. Examples of computer-assisted technologies for lung cancer differential diagnosis are given that exhibit considerable increase in diagnostic accuracy. Multimodal image processing and data fusion in lung cancer diagnosis are discussed and a 'road map' for examining lung cancer patients is suggested on the basis of clinical experience in the use of different modalities for lung cancer staging.

Optimal filter approximation by means of a phase-only filter with quantization.

Approximate filters based on a phase-only filter for reliable recognition of objects are proposed. Good light efficiency and discrimination capability close to that of the optimal filter can be obtained. Computer simulation results are presented and discussed.

Deconvolution of multiple images of the same object.

Deconvolution of images of the same object from multiple sensors with different point spread functions as suggested by Berenstein [Proc. IEEE 78, 723 (1990); Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, S. Chen, ed., Proc. Soc. Photo-Opt. Instrum. Eng. 1569, 35 (1991)], opens new opportunities in solving the image-deconvolution problem, which has challenged researchers for years. We attack this problem in a more realistic formulation than that used by Berenstein; it explicitly takes into account image sensor noise and the necessity for adaptive restoration with estimation of all required signal and noise parameters directly from the observed noisy signals. We show that arbitrary restoration accuracy can be achieved by the appropriate choice of the number of sensor channels and the signal-to-noise ratio in each channel. The results are then extended to the practically important situation when true images in different sensor channels are not identical.

Target location measurement by optical correlators: a performance criterion: comment.

Fleisher et al. [Appl. Opt. 31, 230-233 (1992)] presented an important step toward understanding and designing optimal devices for a target location. However, their results are not complete. Similar considerations, in more complete form, have already been published in Russian.