Refraction causes random dynamic distortions in atmospheric turbulence and in views across a water interface. The latter scenario is experienced by submerged animals seeking to detect prey or avoid predators, which may be airborne or on land. Man encounters this when surveying a scene by a submarine or divers while wishing to avoid the use of an attention-drawing periscope. The problem of inverting random refracted dynamic distortions is difficult, particularly when some of the objects in the field of view (FOV) are moving. On the other hand, in many cases, just those moving objects are of interest, as they reveal animal, human, or machine activity. Furthermore, detecting and tracking these objects does not necessitate handling the difficult task of complete recovery of the scene. We show that moving objects can be detected very simply, with low false-positive rates, even when the distortions are very strong and dominate the object motion. Moreover, the moving object can be detected even if it has zero mean motion. While the object and distortion motions are random and unknown, they are mutually independent. This is expressed by a simple motion feature which enables discrimination of moving object points versus the background.

The possibility of synthesizing light fields satisfying given requirements within a three-dimensional (3-D) space domain was proposed and demonstrated during recent years. In this paper we present fundamental physical properties characterizing 3-D fields and propose analytical and numerical procedures to synthesize them. These methods solve the proper wave equation under 3-D constraints. Since the manipulation of light is a basic task, this is an effective approach to deal with classical as well as novel problems in optics and photonics technologies. We discuss exciting new potential areas of application and extensions of this concept.

We present a method to recover scenes deteriorated by superposition of transparent and semi-reflected contributions, as appear in reflections of windows. Separating the superimposed contributions from the images in which either contribution is in focus is based on mutual blurring and subtraction of the perturbing components. This procedure requires the defocus blur kernels to be known. The use of uncalibrated kernels had previously led to contaminated results. We propose a method for self-calibration of the blur kernels from the raw images themselves. The kernels are sought to minimize the mutual information of the recovered layers. This relaxes the need for prior knowledge on the optical transfer function. Experimental results are presented.

When a transparent surface is present between an observer and an object, an image reflected by the surface may be superimposed on the image of the observed object. We present a new approach to recover the scenes (layers) and to classify which is the reflected/transmitted one, based on imaging through a polarizing filter at two orientations. Estimates of the separate layers are obtained by weighted pixel-wise differences of these images, inverting the image formation process. However the weights depend on the angle of incidence, hence on the inclination of the transparent (invisible) surface. This angle is estimated by seeking the angle-value which (through the weights) leads to decorrelation of the estimated layers. Experimental results, obtained using real photos of actual objects, demonstrate the success of angle estimation and consequent layer separation and labeling. The method is shown to be superior to earlier methods where only raw optical data was used.

In range sensing with time-multiplexed structured light, there is a trade-off between accuracy, robustness and the acquisition period. In this paper a novel structured light method is described. Adaptation of the number and form of the projection patterns to the characteristics of the scene takes place as part of the acquisition process. Noise margins are matched to the actual noise level, thus reducing the number of projection patterns to the necessary minimum. Color is used for light plane labeling. The dimension of the pattern space are thus increased without raising the number of projection patterns. It is shown that the color of an impinging light plane can be identified from the image of the illuminated scene, even with colorful scenes. Identification is local and does not rely on spatial color sequences. The suggested approach has been implemented and the theoretical results are supported by experiments.

A generalized algorithm is introduced to mitigate difficulties encountered in practical implementations of the conventional projections-onto-constraint-sets algorithm. The theory and framework of this new procedure are set, accompanied with an illustration from pattern recognition.