Adaptive phase distortion correction in strong speckle-modulation conditions.

We introduce beam-quality metrics for adaptive wave-front control that permit estimation of the degree of laser beam energy concentration on a remotely located extended object based upon the backscattered wave intensity distribution at the receiver. A 37-control-channel adaptive optics system with phase correction of the output wave capable of operating in the presence of speckle-field-induced strong intensity modulation is presented. System operation is based on optimization of the speckle-field-based metric by the stochastic parallel gradient descent technique. Results demonstrate that adaptive wave-front correction using speckle-field-based beam-quality metrics can significantly improve laser beam concentration on extended objects.

Adaptive optics with advanced phase-contrast techniques. II. High-resolution wave-front control.

A wave-front control paradigm based on gradient-flow optimization is analyzed. In adaptive systems with gradient-flow dynamics, the output of the wave-front sensor is used to directly control high-resolution wavefront correctors without the need for wave-front phase reconstruction (direct-control systems). Here, adaptive direct-control systems with advanced phase-contrast wave-front sensors are analyzed theoretically, through numerical simulations, and experimentally. Adaptive system performance is studied for atmospheric-turbulence-induced phase distortions in the presence of input field intensity scintillations. The results demonstrate the effectiveness of this approach for high-resolution adaptive optics.

Anisoplanatic imaging through turbulent media: image recovery by local information fusion from a set of short-exposure images.

A wide-field-of-view white-light imaging experiment with artificially generated turbulence layers located between the extended object and the imaging system is described. Relocation of the turbulence sources along the imaging path allowed the creation of controllable anisoplanatic effects. We demonstrate that the recently proposed synthetic imaging technique [J. Opt. Soc. Am. A 16, 1623 (1999)] may result in substantial improvement in image quality for highly anisoplanatic conditions. It is shown that for multisource objects located at different distances the processing of turbulence-degraded short-exposure images may lead to a synthetic image that has an image quality superior to that of the undistorted image obtained in the absence of turbulence (turbulence-induced image quality enhancement).

Synthetic imaging: nonadaptive anisoplanatic image correction in atmospheric turbulence.

We introduce a synthetic-imaging technique that can be applied to correct anisoplanatic images degraded by atmospheric turbulence. This method is based on local image-quality analysis applied to a large set of short-exposure images and can be considered a generalization of the frame-selection technique. Experimental results obtained for atmospheric data demonstrate the efficiency of the synthetic-imaging technique in improving image quality.

Adaptive phase-distortion correction based on parallel gradient-descent optimization.

We describe an adaptive wave-front control technique based on a parallel stochastic perturbation method that can be applied to a general class of adaptive-optical system. The efficiency of this approach is analyzed numerically and experimentally by use of a white-light adaptive-imaging system with an extended source. To create and compensate for static phase distortions, we use 127-element liquid-crystal phase modulators. Results demonstrate that adaptive wave-front correction by a parallel-perturbation technique can significantly improve image quality.

Adaptive imaging system for phase-distorted extended source and multiple-distance objects.

We introduce an incoherent adaptive imaging system based on optimization of an image quality metric measured using a coherent optical system. Experimental results and numerical simulations are presented that demonstrate adaptive correction of phase-distorted extended source images containing objects located at multiple distances.

Optimum classification of correlation-plane data by Bayesian decision theory.

A multimodal model for correlation-plane distributions generated by composite filters is presented. From this model a statistical classifier referred to as a composite Bayesian classifier is developed. By exploiting the Gaussian behavior of correlation-plane data, this classifier concisely represents multimodal distributions as composite algebraic functions. These multimodal distributions, each of which is constructed by superposition of many normal distributions, are used to partition a vector signal space into optimum classification regions derived from Bayes's likelihood ratio test. For the purpose of validating the multimodal model, expected performance for the training images is derived from calibration data and compared with observed performance.