$binary phase mask iterative algorithm laser diffraction$

#Binary phase mask iterative algorithm laser diffraction full

The cumbersome configuration procedures and low computational efficiency therefore lead to defects in terms of the real-time performance and the stability of these imaging systems.ĭeep learning, a rapidly developing artificial intelligence technology, has shown great potential for application in various imaging techniques, ranging from image-based wavefront sensing, holographic image reconstruction, coherent diffraction imaging, to inversion of multiple scattering. Despite this, the conventional iterative approaches require tedious system alignment or suffer from the inherent iteration stagnation problems. For starting an iterative process, the intensity of the multiplane defocused images or a single diffraction intensity distribution with known a priori of the sample should be taken. The phase retrieval iterative algorithms widely used in optical imaging are known as the Gerchberg–Saxton algorithm (GSA) and its improved algorithms. CDI- and CS-based methods primarily implement phase estimation with the so-called phase retrieval algorithm or sparsity-based optimization algorithm. Among them, TIE is simple in calculation due to its analytical solution, but it requires multiple intensity measurements and usually has the problem of being sensitive to the map boundaries. In contrast, there are several reference-free conventional phase imaging methods, such as the transport of intensity equation (TIE)-, coherent diffraction imaging (CDI)-, and compressive sensing (CS)-based methods. Nevertheless, most of the basic interferometric systems for holographic recording require the introduction of an additional reference arm otherwise, the reconstruction is disturbed by twin images, and thus the optical configuration is difficult to simplify and lacks flexibility.

#Binary phase mask iterative algorithm laser diffraction full

DH makes full use of photoelectric imaging techniques and computer techniques, and it has steadily developed and gradually matured in the fields of medical diagnosis, dynamic three-dimensional display, information encryption, and so on. The most typical method of recording and restoring phase information is known as digital holography (DH) or digital holographic microscopy (DHM), which is based on interferometry. However, since the available light sensors can only record the intensity of the signal, the ingenious phase recording method and phase retrieval process from the intensity measurement are necessary. Particularly, for objects that are transparent or have uniform amplitude transmittance, reconstructing the wavefront can visualize the spatial distribution of effective information such as thickness and refractive index. Phase imaging focuses on obtaining spatial phase distribution of objects in specific application scenarios, for instance, cell microscopic imaging in biomedicine and wavefront aberration detection in atmospheric optics. In addition to the advantages of efficiency and simplicity compared to now available imaging methods, our model provides significant compressibility for imaging data and can therefore facilitate the low-cost detection and efficient data transmission. Our experiments demonstrate the faithful reconstruction with reasonable quality utilizing a single binary pattern and verify the high redundancy of the information in the intensity measurement for phase recovery. Through the trained network, the phase profile of the object can be extracted from the discrete grains distributed in the low-bit-depth pattern. A thin diffuser as a preprocessor is placed in front of the image sensor to implicitly encode the incoming wavefront information into the distortion and local variation of the generated speckles. In this paper, we present an end-to-end compressible phase imaging method based on deep neural networks, which can implement phase estimation using only binary measurements. However, there is a demand for multiplane full intensity measurements and iterative propagation process or reliance on reference in most conventional approaches. Phase imaging always deals with the problem of phase invisibility when capturing objects with existing light sensors.

$binary phase mask iterative algorithm laser diffraction$