On the use of deep learning for phase recovery.

Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource ( https://github.com/kqwang/phase-recovery ) for readers to learn more about PR.

Single-shot image restoration via a model-enhanced network with unpaired supervision in an optical sparse aperture system.

We propose a model-enhanced network with unpaired single-shot data for solving the imaging blur problem of an optical sparse aperture (OSA) system. With only one degraded image captured from the system and one "arbitrarily" selected unpaired clear image, the cascaded neural network is iteratively trained for denoising and restoration. With the computational image degradation model enhancement, our method is able to improve contrast, restore blur, and suppress noise of degraded images in simulation and experiment. It can achieve better restoration performance with fewer priors than other algorithms. The easy selectivity of unpaired clear images and the non-strict requirement of a custom kernel make it suitable and applicable for single-shot image restoration of any OSA system.

Acceleration of autofocusing with improved edge extraction using structure tensor and Schatten norm.

Determining the optimal focal plane amongst a stack of blurred images in a short response time is a non-trivial task in optical imaging like microscopy and photography. An autofocusing algorithm, or in other words, a focus metric, is key to effectively dealing with such problem. In previous work, we proposed a structure tensor-based autofocusing algorithm for coherent imaging, i.e., digital holography. In this paper, we further extend the realm of this method in more imaging modalities. With an optimized computation scheme of structure tensor, a significant acceleration of about fivefold in computation speed without sacrificing the autofocusing accuracy is achieved by using the Schatten matrix norm instead of the vector norm. Besides, we also demonstrate its edge extraction capability by retrieving the intermediate tensor image. Synthesized and experimental data acquired in various imaging scenarios such as incoherent microscopy and photography are demonstrated to verify the efficacy of this method.

Analysis of the noise in backprojection light field acquisition and its optimization.

Light field reconstruction from images captured by focal plane sweeping can achieve high lateral resolution comparable to the modern camera sensor. This is impossible for the conventional micro-lenslet-based light field capture systems. However, the severe defocus noise and the low depth resolution limit its applications. In this paper, we analyze the defocus noise in the focal-plane-sweeping-based light field reconstruction technique, and propose a method to reduce the defocus noise. Both numerical and experimental results verify the proposed method.

Automatic focusing for multisectional objects in digital holography using the structure tensor.

Determining the axial position of the recorded object in digital holography is a crucial step for image reconstruction. When multiple discrete sections of a three-dimensional object are overlapping each other, this issue becomes more challenging. In this Letter, an autofocusing algorithm using the structure tensor and its eigenvalues is proposed. This method can extract the focal distance of each section for a multi-sectional object irrespective of whether the sections are overlapping or not. We validate the applicability of the proposed technique with synthesized and experimental data using two types of holographic systems.

High-resolution Fourier hologram synthesis from photographic images through computing the light field.

We present a technique for synthesizing the Fourier hologram of a three-dimensional scene from its light field. The light field captures the volumetric information of an object, and an important advantage is that it does not require coherent illumination, as in conventional holography. In this work, we show how to obtain a high-resolution digital hologram with the light field obtained from a series of photographic images captured along the optical axis. The method is verified both by simulations and experimentally captured light field.

Extended focused imaging and depth map reconstruction in optical scanning holography.

In conventional microscopy, specimens lying within the depth of field are clearly recorded whereas other parts are blurry. Although digital holographic microscopy allows post-processing on holograms to reconstruct multifocus images, it suffers from defocus noise as a traditional microscope in numerical reconstruction. In this paper, we demonstrate a method that can achieve extended focused imaging (EFI) and reconstruct a depth map (DM) of three-dimensional (3D) objects. We first use a depth-from-focus algorithm to create a DM for each pixel based on entropy minimization. Then we show how to achieve EFI of the whole 3D scene computationally. Simulation and experimental results involving objects with multiple axial sections are presented to validate the proposed approach.