KEYWORDS: Scattering, Education and training, Gallium nitride, Image restoration, Deep learning, Color imaging, Signal to noise ratio, Light scattering, Biological imaging, Lithium
This paper presents a color computational ghost imaging scheme through a dynamic scattering medium based on deep learning that uses a sole single-pixel detector and is trained by a simulated data set. Due to the color distortion and noise sources being caused by the scattering medium and detector, a simulation data generation method is proposed accordingly that easily adapts to the actual environment. Adequate simulation data sets allow the trained artificial neural networks to exhibit strong reconfiguration capabilities for optical imaging results. It is worth noting that the network trained by our method can reconstruct better details of the image than the simulation data sets according to the ideal state. Its effectiveness is demonstrated in optical imaging experiments with both rotated double-sided frosted glass and a milk solution used as the dynamic scattering medium.
In this paper, a block reconstruction method of object image based on compressed sensing(CS) and orthogonal modulation is presented. Using this method, the amount of data processing can be greatly reduced due to the application of CS theory and it brings convenience for post-processing. The method can be utilized especially when we just need to reconstruct partial of a huge image, because the orthogonal basis matrix can extract the measurements of corresponding block, and then the needed partial image can be reconstructed directly instead of reconstructing the whole huge image at first. Therefore, this method can reduce the redundant computation in process of reconstruction. And the total amount of calculation is also greatly reduced. The feasibility is verified by results of an experiment, in which we use a video projector to incorporate the random measurement matrix into the system.
A pinhole is usually used as the pupil in traditional optical scanning holography (OSH) method. Although such a structure is relatively simple, the in-focus sectional image may be degraded by out-of-focus haze because of its difficulty to be eliminated in sectional image reconstruction. In this paper, a random-phase pupil is employed in OSH system to reduce the impact of defocus image. It is proved by the experimental results that the defocus image trends to be more easily dispersed into speckle-like pattern, and then it can be removed by connected component method in the future. Analysis also focuses on the correlation coefficient between the original image and the reconstructed images under the conditions of adopting a pinhole or a random-phase pupil. From comparison, as the defocus distance increasing, one can find that the correlation coefficient of image by using a random-phase pupil is decreasing faster than using a pinhole pupil.
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