Twin Image Removal Research Articles

Adaptive support constraint based on pixel difference distribution for twin-image removal in in-line holography

A tight support domain can enhance the reconstruction effect of phase retrieval algorithms using a support domain constraint. Hence, an adaptive support method based on pixel difference distribution to calculate a more accurate support domain is proposed in this paper. The support domain is continuously updated at each iteration of the phase retrieval algorithm, resulting in a support domain that closely corresponds to the shape and size of the object. Simulations are conducted to investigate the reconstruction effect of complex-valued objects as well as the influence of varying the binarization threshold and the difference window size on the convergence performance in the absence of noise. The changing dynamics of the support domain during the iterative process are also showcased. The experiments demonstrate that the adaptive support method can improve the reconstruction image quality and image contrast under actual conditions. Both simulations and experiments affirm the feasibility of this approach.

Physics-driven universal twin-image removal network for digital in-line holographic microscopy.

Digital in-line holographic microscopy (DIHM) enables efficient and cost-effective computational quantitative phase imaging with a large field of view, making it valuable for studying cell motility, migration, and bio-microfluidics. However, the quality of DIHM reconstructions is compromised by twin-image noise, posing a significant challenge. Conventional methods for mitigating this noise involve complex hardware setups or time-consuming algorithms with often limited effectiveness. In this work, we propose UTIRnet, a deep learning solution for fast, robust, and universally applicable twin-image suppression, trained exclusively on numerically generated datasets. The availability of open-source UTIRnet codes facilitates its implementation in various DIHM systems without the need for extensive experimental training data. Notably, our network ensures the consistency of reconstruction results with input holograms, imparting a physics-based foundation and enhancing reliability compared to conventional deep learning approaches. Experimental verification was conducted among others on live neural glial cell culture migration sensing, which is crucial for neurodegenerative disease research.

Single-shot experimental-numerical twin-image removal in lensless digital holographic microscopy

Lensless digital holographic microscopy (LDHM) offers very large field of view label-free imaging crucial, e.g., in high-throughput particle tracking and biomedical examination of cells and tissues. Compact layouts promote point-of-case and out-of-laboratory applications. The LDHM, based on the Gabor in-line holographic principle, is inherently spoiled by the twin-image effect, which complicates the quantitative analysis of reconstructed phase and amplitude maps. Popular family of solutions consists of numerical methods, which tend to minimize twin-image upon iterative process based on data redundancy. Additional hologram recordings are needed, and final results heavily depend on the algorithmic parameters, however. In this contribution we present a novel single-shot experimental-numerical twin-image removal technique for LDHM. It leverages two-source off-axis hologram recording deploying simple fiber splitter. Additionally, we introduce a novel phase retrieval numerical algorithm specifically tailored to the acquired holograms, that provides twin-image-free reconstruction without compromising the resolution. We quantitatively and qualitatively verify proposed method employing phase test target and cheek cells biosample. The results demonstrate that the proposed technique enables low-cost compact LDHM imaging with enhanced precision, achieved through the elimination of twin-image errors. This advancement opens new avenues for more accurate technical and biomedical imaging applications using LDHM, particularly in scenarios where cost-effective and portable imaging solutions are desired.

Randomness assisted in-line holography with deep learning

We propose and demonstrate a holographic imaging scheme exploiting random illuminations for recording hologram and then applying numerical reconstruction and twin image removal. We use an in-line holographic geometry to record the hologram in terms of the second-order correlation and apply the numerical approach to reconstruct the recorded hologram. This strategy helps to reconstruct high-quality quantitative images in comparison to the conventional holography where the hologram is recorded in the intensity rather than the second-order intensity correlation. The twin image issue of the in-line holographic scheme is resolved by an unsupervised deep learning based method using an auto-encoder scheme. Proposed learning technique leverages the main characteristic of autoencoders to perform blind single-shot hologram reconstruction, and this does not require a dataset of samples with available ground truth for training and can reconstruct the hologram solely from the captured sample. Experimental results are presented for two objects, and a comparison of the reconstruction quality is given between the conventional inline holography and the one obtained with the proposed technique.

Deep DIH: Single-Shot Digital In-Line Holography Reconstruction by Deep Learning

Digital in-line holography is commonly used to reconstruct 3D images from 2D holograms for microscopic objects. One of the technical challenges that arise in the signal processing stage is removing the twin image that is caused by the phase-conjugate wavefront from the recorded holograms. Twin image removal is typically formulated as a non-linear inverse problem due to the irreversible scattering process when generating the hologram. Recently, end-to-end deep learning-based methods have been utilized to reconstruct the object wavefront (as a surrogate for the 3D structure of the object) directly from a single-shot in-line digital hologram. However, massive data pairs are required to train deep learning models for acceptable reconstruction precision. In contrast to typical image processing problems, well-curated datasets for in-line digital holography does not exist. Also, the trained model highly influenced by the morphological properties of the object and hence can vary for different applications. Therefore, data collection can be prohibitively cumbersome in practice as a major hindrance to using deep learning for digital holography. In this paper, we proposed a novel implementation of autoencoder-based deep learning architecture for single-shot hologram reconstruction solely based on the current sample without the need for massive datasets to train the model. The simulations results demonstrate the superior performance of the proposed method compared to the state of the art single-shot compressive digital in-line hologram reconstruction method.

Hybrid algorithm for twin image removal in optical scanning holography

Optical scanning holography is an incoherent optical image processing system. It is a technique, where the complete information of an object or image will be recorded as a hologram and later reconstructed to get back the original image. In the hologram reconstruction process, a virtual image is formed along with the real image, which appears as a twin image noise. To eliminate such noises, a technique of hybrid algorithm is used while recording the hologram itself. The MATLAB R2012b image processing tool is used for simulation and the simulated values are tabulated and compared with the existing method in terms of peak signal to noise ratio, mean square error. In the reconstruction, the proposed method results are having 26% increment in the MSE and PSNR values. To further improve the MSE and PSNR values a case study using different denoising techniques combined with the proposed hybrid algorithm is used and found considerable improvement of 32%. Hence the image quality is increased.

Hybrid Algorithm for Twin Image Removal in Optical Scanning Holography

Optical scanning holography is an incoherent optical image processing system. It is a technique, where the complete information of an object or image will be recorded as a hologram and later recons...

Twin image removal in digital in-line holography based on iterative inter-projections

A simple and efficient phase retrieval method based on the iterative inter-projections of the recorded Fourier modulus between two effective holographic planes is developed to eliminate the twin image in digital in-line holography. The proposed algorithm converges stably in phase extraction procedures without requiring any prior knowledge or sophisticated support of the object and is applicable to lensless Gabor and Fourier holography as well as holographic microscopy with imaging lenses. Numerical and experimental results suggest that the spatial resolution enhancement on the reconstructed image can be achieved with this technique due to the capability of recovering the diffraction phases of low-intensity signals.

A new wavelet-based reconstruction algorithm for twin image removal in digital in-line holography

Two original methods are proposed here for digital in-line hologram processing. Firstly, we propose an entropy-based method to retrieve the focus plane which is very useful for digital hologram reconstruction. Secondly, we introduce a new approach to remove the so-called twin images reconstructed by holograms. This is achieved owing to the Blind Source Separation (BSS) technique. The proposed method is made up of two steps: an Adaptive Quincunx Lifting Scheme (AQLS) and a statistical unmixing algorithm. The AQLS tool is based on wavelet packet transform, whose role is to maximize the sparseness of the input holograms. The unmixing algorithm uses the Independent Component Analysis (ICA) tool. Experimental results confirm the ability of convolutive blind source separation to discard the unwanted twin image from in-line digital holograms.

Digital in-line particle holography: twin-image suppression using sparse blind source separation

We propose a robust autofocus method for reconstructing digital holograms and twin-image removal based on blind source separation approach. The method is made up of two components: an efficient quincunx lifting scheme based on wavelet packet transform, whose role is to maximize a sharpness metric related to the sparseness of the input holograms, and a geometric unmixing algorithm, which achieves the separation task. Experimental results confirm the ability of sparse blind source separation to discard the unwanted twin-image from in-line digital holograms of particles.

Twin-image problem in digital holography—a survey (Invited Paper)

Zero-order and twin images are a serious obstacle in achieving a high-quality output in in-line digital holography (DH). They decrease the useful bandwidth of the off-axis DH. Over the years the twin image removal problem was approached both by instrumental and numerical means. The paper provides an extended survey of the proposed solutions with their pros and cons as a guide for further advance in this field. Processing of a single spatial carrier fringe pattern involves spatial filtering in the frequency domain, spatial phase-shifting (PS) or wavelet transform. A point source digital holographic microscopy (DHM), introduction of calibration measurements or various modifications of PS technique are instrumental solutions to the twin image problem for in-line DH. Numerical solutions to the same problem include iterative and non-iterative approaches, diffraction-based and inverse problem solutions, reconstruction of purely real or phase objects and of complex objects, reconstruction of plane and volume objects. Elimination only of the zero-order image relies on non-linear filtering or additional calibration measurements.

Cascaded adaptive-mask algorithm for twin-image removal and its application to digital holograms of ice crystals

An iterative Gerchberg-Saxton-type algorithm with a support constraint for twin-image removal from reconstructed Gabor inline holograms of single plane objects is described. It is applied to simulated holograms and to holograms of ice crystals recorded in the laboratory and in atmospheric clouds in situ. The algorithm is characterized by a distinction between object and background region and an iterative adaption of the object mask. Applying the algorithm to recorded inline holograms of atmospheric objects, the twin-image artifacts are removed successfully, for the first time allowing for a proper access to the in situ phase information on atmospheric ice crystals. It is also demonstrated that, after application of the algorithm, previously indiscernible internal object features can become visible for large Fresnel numbers.

Twin image removal in X-ray fluorescence holography with two energies

In the last decade, X-ray fluorescence holography has been developed for the study of 3D atomic arrangements in solids. However, it encounters the twin image problem which may disturb the reconstructed atomic images. In this paper, the formation of twin image is discussed and we propose a modified two-energy algorithm to remove the twin image. The simulation shows that the method is valid and more efficient than the multiple-energy algorithm proposed by Barton.

Two-energy twin image removal in atomic-resolution x-ray holography

We propose a two-energy twin image removal algorithm for atomic-resolution x-ray holography. The validity of the algorithm is shown in a theoretical simulation and in an experiment of internal detector x-ray holography using a ZnSe single crystal. The algorithm, compared to the widely used multiple-energy algorithm, allows efficient measurement of holograms, and is especially important when the available x-ray energies are fixed. It enables twin image free holography using characteristic x rays from laboratory generators and x-ray pulses of free-electron lasers.