Vortex beams have been applied in multiple engineering and scientific applications due to their distinctive orbit angular momentum and vortex phase characteristics. Traditional methods suffer from complicated setup for the generation of vortex beams not only costly but also non-scalable. In this study, we propose a novel approach for the direct generation of vortex beams and coaxial muti-vortex beams through the use of laser resonator mirror. We have designed a diffractive output mirror of the laser resonator using Gerchberg-Saxton (GS) algorithm inspired by the mode matching theory and optical diffraction principles. Through the analysis of the pumping power in a four-level laser configuration, we have established correlations between ring-shaped pumping light and target vortex beam characteristics, thereby providing essential design guidelines for vortex beam generation within resonators.
The research proposes arbitrary shaped aperture wavefront fitting (ASAWF) for arbitrary shaped aperture wavefront fitting and aberration removing. It departs from the traditional Zernike polynomials only ideal for wavefront reconstruction of circular wavefronts but not non-circular wavefronts. The approach preserves the orthogonality of the Zernike polynomials in arbitrary shaped aperture wavefronts and addresses the coefficient coupling problems of the non-circular shaped aperture wavefront fitting known adversely affecting aberration removal. The ASAWF method is used to fit non-circular regions with an orthogonal matrix using the modified Gram–Schmidt orthogonalization technique with QR decomposition and point cloud. The ASAWF method is employed to fit the arbitrary shaped aperture wavefront, including elliptical, square, and irregular shape wavefronts. The proposed method is confirmed through numerical simulation and experimental data demonstrating the effectiveness of wavefront reconstruction and aberration removal.
Processing of face images is an important branch of machine vision. In real scenarios, the quality of the acquired images often does not reach the ideal condition therefore producing wrong results. While face super-resolution and rotation are two different ways to enhance the quality of face images, these two methods have always existed independently. The super resolution of face is to improve the image quality and get high resolution image from low resolution face image, and the rotation of face is to get the image under different view of the target and enrich the target person information. Both techniques have different approaches, but both are designed to improve the image quality for easier processing such as classification, detection and recognition. Therefore, can we combine the two methods, convert an image from a low-resolution image to a high-resolution image, and then use face rotation to obtain images of other views of the target face. We can obtain a higher quality image compared with the two independent methods. In this paper, we use a supervised learning method for face super-resolution and a self-supervised face rotation method for combining experiments, and the results show the reliability of combining the two methods.
KEYWORDS: Holograms, Denoising, Education and training, Speckle, Image processing, Holography, Digital holography, Histograms, Deep learning, 3D image reconstruction
Digital holographic microscopy (DHM) is a non-contact and high accuracy measurement technique widely used in biomedicine, microstructure,and other fields.The quality of the reconstructed image and the effectiveness of holographic microscopy were easily affected by speckle noise. Inspired by the idea of Noise2Noise, we propose a self-supervised noise2noise hologram speckle noise removal method. From the holograms that need denoising to generate the input and labels with the same noise distribution to form a training pair for training. Solve the problem that clean holograms are difficult to obtain.The training sets of this self-supervised method are generated from the holograms to be processed. As such, it avoids the need of collectting a large number of training sets. The proposed method is therefore less vulnerable to the background noises and more convenient and reliable for practical hologram speckle denoising applications.
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