A hybrid imaging system is a simultaneous physical arrangement of a refractive lens and a multilevel phase mask (MPM) as a diffractive optical element (DOE). The favorable properties of the hybrid setup are improved extended-depth-of-field (EDoF) imaging and low chromatic aberrations. We built a fully differentiable image formation model in order to use neural network techniques to optimize imaging. At the first stage, the design framework relies on the model-based approach with numerical simulation and end-to-end joint optimization of both MPM and imaging algorithms. In the second stage, MPM is fixed as found at the first stage, and the image processing is optimized experimentally using the CNN learning-based approach with MPM implemented by a spatial light modulator. The paper is concentrated on a comparative analysis of imaging accuracy and quality for design with various basic optical parameters: aperture size, lens focal length, and distance between MPM and sensor. We point out that the varying aperture size, lens focal length, and distance between MPM and sensor are for the first time considered for end-to-end optimization of EDoF. We numerically and experimentally compare the designs for visible wavelength interval [400-700]~nm and the following EDoF ranges: [0.5-100]~m for simulations and [0.5-1.9]~m for experimental tests. This study concerns an application of hybrid optics for compact cameras with aperture [5-9] mm and distance between MPM and sensor [3-10] mm.
Optimal optical transfer function (OTF) is proposed for RGB inverse imaging with extended depth of field (DoF) and diminished color aberrations. This optimal OTF is derived as the Wiener filter of broadband defocused OTFs. The performance of this new inverse imaging is demonstrated for optical setups with lens and lensless with a multilevel phase mask instead of the lens. The later lensless system designed for the wavelength range (400 to 700) nm and DoF range (0.5 to 1000) m demonstrates the best performance.
Double-stage delay-multiply-and-sum (DS-DMAS) is one of the algorithms proposed for photoacoustic image reconstruction where a linear-array transducer is used to detect signals. This algorithm provides a higher contrast image in comparison with the conventional delay-multiply-and-sum (DMAS) and delay-and-sum (DAS), but it imposes a high computational complexity. In this paper, open accelerators (OpenACC) GPU computation parallel approach is used to lessen the computational time and address the high computational time of the DSDMAS for photoacoustic image reconstruction process. Compared with sequential execution of the DS-DMAS on CPU, a speed-up of approximately 74× is achieved (for an image having 1024 × 1024 pixels). The proposed approach provides possibility to have an accurate reconstructed photoacoustic image with a reasonable frame rate. In addition, the higher the number of the image pixels, the higher speed-up is achieved. Using the suggested GPU implementation, it is feasible to reconstruct photoacoustic images having a size of 128 × 128, and 256 × 256 with a frame rate of 3 and 2, respectively.
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