No abstract

Modern consumer electronics market dictates the need for small-scale and high-performance cameras. Such designs involve trade-offs between various system parameters. In such trade-offs, Depth Of Field (DOF) is a significant issue very often. We propose a computational imaging-based technique to overcome DOF limitations. Our approach is based on the synergy between a simple phase aperture coding element and a convolutional neural network (CNN). The phase element, designed for DOF extension using color diversity in the imaging system response, causes chromatic variations by creating a different defocus blur for each color channel of the image. The phase-mask is designed such that the CNN model is able to restore from the coded image an all-in-focus image easily. This is achieved by using a joint end-to-end training of both the phase element and the CNN parameters using backpropagation. The proposed approach provides superior performance to other methods in simulations as well as in real-world scenes.

Motion-related image blur is a known issue in photography. In practice, it limits the exposure time while capturing moving objects; thus, achieving proper exposure is difficult. Extensive research has been carried out to compensate for it, to allow increased light throughput without motion artifacts. In this work, a joint optical-digital processing method for motion deblurring is proposed and demonstrated. Using dynamic phase coding in the lens aperture during the image acquisition, the motion trajectory is encoded in an intermediate optical image. This coding embeds cues for both the motion direction and extent by coloring the spatial blur of each object. These color cues serve as guidance for a digital deblurring process, implemented using a convolutional neural network (CNN) trained to utilize such coding for image restoration. Particularly, unlike previous optical coding solutions, our strategy encodes cues with no limitation on the motion direction, and without sacrificing light efficiency. We demonstrate the advantage of the proposed approach over blind deblurring methods with no optical coding, as well as over other solutions that use coded acquisition, in both simulation and real-world experiments.

A recent publication [Opt. Express16, 20540-20561 (2008)] presented a way for extending the depth of field (DOF) of imaging systems using a binary phase mask made of annular rings delivering a π-phase shift. Usually, such masks are designed with respect to some central wavelength; they will thus deliver a different phase shift for other wavelengths. This issue is reexamined in this paper, where it is shown that polychromatic masks that deliver the same phase shift over a wide range of wavelengths provide improved imaging over an extended DOF. The simulation results demonstrate the improved performance of imaging systems using such masks.

Lenses used in many infrared (IR) imaging systems are temperature sensitive. One of the most popular IR optical materials for lens fabrication is germanium; nevertheless, it exhibits a strong temperature dependent refractive index, causing significant thermal focal shift which in turn results in image blur. An all-optical solution for IR lens athermalization with no moving parts based on a thermally dependent binary phase mask is hereby proposed and analyzed. It allows high quality imaging to be obtained for a wide range of temperature variations, with minimal performance degradation at nominal temperature conditions.