End-to-end deep learning framework for digital holographic reconstruction

Abstract

Digital holography records the entire wavefront of an object, including amplitude and phase. To reconstruct the object numerically, we can backpropagate the hologram with Fresnel-Kirchhoff integralbased algorithms such as the angular spectrum method and the convolution method. Although effective, these techniques require prior knowledge, such as the object distance, the incident angle between the two beams, and the source wavelength. Undesirable zero-order and twin images have to be removed by an additional filtering operation, which is usually manual and consumes more time in off-axis configuration. In addition, for phase imaging, the phase aberration has to be compensated, and subsequently an unwrapping step is needed to recover the true object thickness. The former either requires additional hardware or strong assumptions, whereas the phase unwrapping algorithms are often sensitive to noise and distortion. Furthermore, for a multisectional object, an all-in-focus image and depth map are desired for many applications, but current approaches tend to be computationally demanding. We propose an end-to-end deep learning framework, called a holographic reconstruction network, to tackle these holographic reconstruction problems. Through this data-driven approach, we show that it is possible to reconstruct a noise-free image that does not require any prior knowledge and can handle phase imaging as well as depth map generation.

Does deep learning always outperform simple linear regression in optical imaging?

“…In recent years, deep learning receives much attention in many research fields including optical design [1,2] and optical imaging [3]. In previous works, deep learning has been extensively applied for many optical imaging problems including phase retrieval [4][5][6][7], microscopic image enhancement [8][9], scattering imaging [10][11], holography [12][13][14][15][16][17][18], single-pixel imaging [19,20], super-resolution [21][22][23][24], Fourier ptychography [25][26][27], optical interferometry [28,29], wavefront sensing [30,31], and optical fiber communications [32].…”

Single-shot lensless imaging with fresnel zone aperture and incoherent illumination

“…Recently, lensless imaging has become attractive due to its thin and easy-to-build form. In the past few years, various lensless imaging techniques have been proposed with coherent systems, such as an on-chip microscope 9,10 , coherent diffractive imaging 11,12 , and a series of learningbased methods [13][14][15] . Due to the requirement of coherent illumination, however, applications of such systems are limited.…”

Digital Holographic Reconstruction Based on Deep Learning Framework With Unpaired Data

“…There are many algorithms based on CNN for the reconstruction of DH [29], [30], which use the CNN to eliminate the twin image term and get the accurate focusing distance. In order to achieve one-step reconstruction of the hologram, the end-to-end framework is introduced into the algorithm [31], [32]. After learning the relationship between holograms and corresponding complex distribution, the wavefront information can be directly recovered from a single-shot hologram.…”