Image Reconstruction Of Objects Research Articles

Reconstructing images of objects: method for reconstructing images from digital off-axis holograms based on a generative adversarial neural network

The reconstruction of object images that are located in 3D scene cross-sections using digital holography is described. The potential of generative adversarial networks for reconstructing cross-sections of 3D scenes composed of multiple layers of off-axis objects from holograms is investigated. Such scenes consist of a series of sections with objects that are not aligned with the camera’s axis. Digital holograms were used to reconstruct images of cross-sectional views of 3D scenes. It has been shown that the use of neural networks increases the speed and reconstruction quality, and reduces the image noise. A method for reconstructing images of objects using digital off-axis holograms and a generative adversarial neural network is proposed. The proposed method was tested on both numerically simulated and experimentally captured digital holograms. It was able to successfully reconstruct up to 8 cross-sections of a 3D scene from a single hologram. It was obtained that an average structural similarity index measure was equal to at least 0.73. Based on optically registered holograms, the method allowed us to reconstruct object image cross-sections of a 3D scene with a structural similarity index measure over cross-sections of a 3D scene of equal to 0.83. Therefore, the proposed technique provides the possibility for high-quality object image reconstruction and could be utilized in the analysis of micro- and macroobjects, including medical and biological applications, metrology, characterization of materials, surfaces, and volume media.

Improving the Machine Learning Performance for Image Recognition Using a New Set of Mountain Fourier Moments

The orthogonal moments are giving relevant results of these last years within the framework of object detection, pattern recognition and image reconstruction. This article is based on orthogonal functions called "Orthogonal Mountain functions (OMFs)" and we introduce a new set of moments called the multichannel Mountain Fourier moments (MMFMs), their performance is in reconstruction, noise invariants, rotation, scale and translation for image color. To validate these proposed techniques, we made several experimental tests to analyse images. We compare the results obtained from invariant moments and other current orthogonal invariant moments; the experiments show the power of the proposed moments.

Optimal reconstruction and recognition of images by Jacobi Fourier moments and artificial bee colony (ABC) algorithm

The orthogonal moments giving relevant results of these last years within the framework of object detection, pattern recognition and image reconstruction, this work based on orthogonal functions called Orthogonal Jacobi Polynomials (OJPs), and we introduce a new set of moments called Generalized Jacobi Fourier Moments (GJFMs), these polynomials are characterized by parameters . However, it was very important to optimize these parameters in order to obtain a good result, in this context; this study used a new approach to optimized Jacobi Fourier parameters using the artificial bee colony algorithm (ABC) in order to improves the quality of reconstruction of images of large sizes. On the one hand, to validate this technique which offers a high image reconstruction quality. On other hand, the comparison carried out with other algorithms clearly indicates the advantage of the proposed method.

Origin of the super-resolution of microsphere-assisted imaging

Theoretical explanation of the super-resolution imaging by contact microspheres created a point of attraction for nanoimaging research during the last decade with many models proposed, yet its origin remains largely elusive. Using a classical double slit object, the key factors responsible for this effect are identified by an ab initio imaging model comprising object illumination, wave scattering, and image reconstruction from the diffracted far fields. The scattering is found by a full-wave solution of the Maxwell equations. The formation of super-resolved images relies on coherent effects, including the light scattering into the waves circulating inside the microsphere and their re-illumination of the object. Achieving the super-resolution of the double slit requires a wide illumination cone as well as a deeply sub-wavelength object-to-microsphere separation. The resultant image has a significantly better resolution as compared to that from the incoherent imaging theory.

NeuroZoom: Denoising and Super Resolving Neuromorphic Events and Spikes.

Neuromorphic cameras are emerging imaging technology that has advantages over conventional imaging sensors in several aspects including dynamic range, sensing latency, and power consumption. However, the signal-to-noise level and the spatial resolution still fall behind the state of conventional imaging sensors. In this paper, we address the denoising and super-resolution problem for modern neuromorphic cameras. We employ 3D U-Net as the backbone neural architecture for such a task. The networks are trained and tested on two types of neuromorphic cameras: a dynamic vision sensor and a spike camera. Their pixels generate signals asynchronously, the former is based on perceived light changes and the latter is based on accumulated light intensity. To collect the datasets for training such networks, we design a display-camera system to record high frame-rate videos at multiple resolutions, providing supervision for denoising and super-resolution. The networks are trained in a noise-to-noise fashion, where the two ends of the network are unfiltered noisy data. The output of the networks has been tested for downstream applications including event-based visual object tracking and image reconstruction. Experimental results demonstrate the effectiveness of improving the quality of neuromorphic events and spikes, and the corresponding improvement to downstream applications with state-of-the-art performance.

Method of Reconstructing Object Images in Continuous Wave Terahertz Holography for Defectoscopy of Composites

Method of Reconstructing Object Images in Continuous Wave Terahertz Holography for Defectoscopy of Composites

Optical multiple-image authentication method based on Fourier single-pixel imaging and multiple logistic maps.

As a promising technique, the spatial information of an object can be acquired by employing active illumination of sinusoidal patterns in the Fourier single-pixel imaging. However, the major challenge in this field is that a large number of illumination patterns should be generated to record measurements in order to avoid the loss of object details. In this paper, an optical multiple-image authentication method is proposed based on sparse sampling and multiple logistic maps. To improve the measurement efficiency, object images to be authenticated are randomly sampled based on the spatial frequency distribution with smaller size, and the Fourier sinusoid patterns generated for each frequency are converted into binarized illumination patterns using the Floyd-Steinberg error diffusion dithering algorithm. In the generation process of the ciphertext, two chaotic sequences are used to randomly select spatial frequency for each object image and scramble all measurements, respectively. Considering initial values and bifurcation parameters of logistic maps as secret keys, the security of the cryptosystem can be greatly enhanced. For the first time to our knowledge, how to authenticate the reconstructed object image is implemented using a significantly low number of measurements (i.e., at a very low sampling ratio less than 5% of Nyquist limit) in the Fourier single-pixel imaging. The experimental results as well as simulations illustrate the feasibility of the proposed multiple-image authentication mechanism, which can provide an effective alternative for the related research.

Tuning parameters of phase retrieval algorithm for single-shot imaging based on object-modulated speckles by particle swarm optimization

Imaging through scattering media has become an important aspect in optical imaging. To achieve this, different approaches have been presented to recover the phase of an imaging object from its scattered speckle behind a scattering medium. However, all these algorithms require initial parameters which are obtained based on prior knowledge of the imaging object. This is impractical, as prior knowledge of the imaging object is not always available in real imaging applications. In this paper, we propose using a Particle Swarm Optimization algorithm to find these optimal parameters without requiring prior knowledge of the imaging object. These optimal values are then adopted in the Phase Retrieval algorithm based on object-modulated speckles for object image reconstruction behind a scattering medium in a single-shot imaging system. The obtention of the values for the standard deviation for Gaussian filter and the threshold control allows Phase Retrieval to converge faster and avoids the need for running the algorithm more than once in the search for the best reconstruction.

Progressive Tandem Learning for Pattern Recognition With Deep Spiking Neural Networks.

Spiking neural networks (SNNs) have shown clear advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency, due to their event-driven nature and sparse communication. However, the training of deep SNNs is not straightforward. In this paper, we propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition, which is referred to as progressive tandem learning. By studying the equivalence between ANNs and SNNs in the discrete representation space, a primitive network conversion method is introduced that takes full advantage of spike count to approximate the activation value of ANN neurons. To compensate for the approximation errors arising from the primitive network conversion, we further introduce a layer-wise learning method with an adaptive training scheduler to fine-tune the network weights. The progressive tandem learning framework also allows hardware constraints, such as limited weight precision and fan-in connections, to be progressively imposed during training. The SNNs thus trained have demonstrated remarkable classification and regression capabilities on large-scale object recognition, image reconstruction, and speech separation tasks, while requiring at least an order of magnitude reduced inference time and synaptic operations than other state-of-the-art SNN implementations. It, therefore, opens up a myriad of opportunities for pervasive mobile and embedded devices with a limited power budget.

Imaging multi-materials tightly combined objects by applying grey relational analysis in muon tomography

A novel method developed within Muon Scattering Tomography (MST) to location interface of Multi-Materials Tightly Combined (MMTC) objects. MST has high sensitivity to high-Z materials such as uranium, and is very useful for detecting them in a background of low-Z materials. But commonly used algorithms in MST such as the Point of Closest Approach (PoCA) are difficult to location the interface of MMTC objects, limited its application. In this paper, we investigated Grey Relational Analysis (GRA) based on PoCA to obtain the MMCT interface identification. Then, we applied this novel approach to image the layered structure of the molten pool, which may be of reference value for cooling systems. The results show that 5 days measurement time spans can discriminate the two- and three-layered molten pool models. The further study also shows that this method does not require high position resolution detectors. The uncertainty of interface location in molten pool models under the plastic scintillator detector with a 2cm position resolution is less than 10%. This novel method is practical and provides a new and effective idea for image reconstruction of MMTC objects.

Ghost imaging via spectral multiplexing in the broadband terahertz range

A method is proposed for accelerating measurements for ghost imaging reconstruction of objects using spectral multiplexing of speckle patterns formed by broadband terahertz (THz) radiation. The presented method aims at the challenge of a sufficiently large number of measurements, and increases the correlation between reference and reconstructed images. It is experimentally shown that unique speckle patterns for individual frequencies can be formed by broadband THz radiation while propagating through a random phase plate. It is shown by numerical simulation that these speckle patterns can be used to decrease the number of diffusers required by more than one order compared to the integrated method. The possibility of using electro-optical detection in the proposed technique reduces the number of measurements. For example, for the spectral range from 0.2 to 1.2 THz, there is a more than 20-fold decrease in the number of diffusers used.

Improving reconstruction of targets hidden in scattering media by introducing the Lucy-Richardson deconvolution algorithm into a system of multiview optical projections

A method that combines Lucy-Richardson deconvolution algorithm (LRA) with a multiview projection setup to improve imaging reconstruction of objects embedded in scattering media is presented. In the first step, the medium was illuminated with a laser beam and single-shot multiple images of the object were obtained from different viewpoints. Next, the above procedure was performed under the same conditions but only the medium, without the object, was illuminated by a light point source to obtain the system PSF. Third, each sub-image of the object was digitally cropped, extracted from the array, and deconvolved with the average PSF image following the LRA. Finally, all the sub-deconvolved images were shifted to a common point and superimposed to reveal the shape of the hidden object. Various setup conditions including medium turbidity, thickness, target shape, and illumination state were tested and compared. Results indicate an effective performance of the proposed method.

Investigation of image plane for image reconstruction of objects through diffusers via deep learning.

.Significance: The imaging of objects hidden in light-scattering media is a vital practical task in a wide range of applications, including biological imaging. Deep-learning-based methods have been used to reconstruct images behind scattering media under complex scattering conditions, but improvements in the quality of the reconstructed images are required.Aim: To investigate the effect of image plane on the accuracy of reconstructed images.Approach: Light reflected from an object passing through glass diffusers is captured by changing the image plane of an optical imaging system. Images are reconstructed by deep learning, and evaluated in terms of structural similarity index measure, classification accuracy of digital images, and training and testing error curves.Results: The reconstruction accuracy was improved for the case in which the diffuser was imaged, compared to the case where the object was imaged. The training and testing error curves show that the loss converged to lower values in fewer epochs when the diffuser was imaged.Conclusions: The proposed approach demonstrates an improvement in the accuracy of the reconstruction of objects hidden through glass diffusers by imaging glass diffuser surfaces, and can be applied to objects at unknown locations in a scattering medium.

Accurate EOM-based phase-shifting digital holography with a monitoring interferometer.

Phase-shifting digital holography (PSDH) can effectively remove the zero-order term and twin image in on-axis holography, but the phase-shifting error deteriorates the quality of reconstructed object images. In this paper, accurate PSDH with an electro-optic modulator (EOM) is proposed. The EOM is used to generate the required phase shift of on-axis digital holography, and the required phase shift is precisely measured with orthogonal detection of a homodyne interferometer and controlled with proportional-integral-derivative feedback in real time. The merits of our method are that it can achieve fast and accurate phase shifting without mechanical motion or sacrificing the resolution and field of view. The optical configuration was designed, an experimental setup was constructed, and real-time phase shifting was realized. Experiments of the phase-shifting accuracy evaluation, suppression effectiveness of the zero-order and twin image terms, and the specimen measurement demonstrate that the proposed method has significant application for precision topography measurement.

Doppler Division Multiplexed Multiple-Input–Multiple-Output Imaging Using Cascaded Millimeter-Wave Radars

Radar-based near-field 3-D imaging technology has recently attracted increasing attention in the field of security systems. This article presents a novel multiple-input–multiple-output (MIMO) synthetic aperture radar imaging algorithm for security checkpoints attached to a moving walkway. The proposed algorithm is characterized by transmitting-signal multiplexing on the Doppler frequency domain, which allows for a reduced data acquisition time compared with that of conventional time-division multiplexed MIMO systems and therefore achieves the image reconstruction of dangerous objects with smaller motion errors. This algorithm can be applied to 1-D antenna array systems, and image reconstruction is based on a back-projection algorithm with some modifications. The mathematical derivation of the algorithm and its advantages and disadvantages in comparison with those of a conventional system are detailed in this article. Details of numerical simulation of a point target as well as the experimental results of image reconstruction of a pinball and toy gun, using cascaded millimeter-wave sensors, are also presented for verifying the performance of the algorithm.

Complete Detection and Reconstruction of Object Image in Image Plane Digital Holography

Complete Detection and Reconstruction of Object Image in Image Plane Digital Holography

Non-line-of-sight imaging under white-light illumination: a two-step deep learning approach.

Non-line-of-sight (NLOS) imaging has received considerable attentions for its ability to recover occluded objects from an indirect view. Various NLOS imaging techniques have been demonstrated recently. Here, we propose a white-light NLOS imaging method that is equipped only with an ordinary camera, and not necessary to operate under active coherent illumination as in other existing NLOS systems. The central idea is to incorporate speckle correlation-based model into a deep neural network (DNN), and form a two-step DNN strategy that endeavors to learn the optimization of the scattered pattern autocorrelation and object image reconstruction, respectively. Optical experiments are carried out to demonstrate the proposed method.

Single-shot x-ray phase-contrast and dark-field imaging based on coded binary phase mask

We introduce a coded-mask-based multi-contrast imaging method for high-resolution phase-contrast and dark-field imaging. The method uses a binary phase mask designed to provide an ultra-high-contrast pattern and reference-free single-shot measurement and an algorithm based on maximum-likelihood optimization and automatic differentiation to perform simultaneous reconstruction of absorption, phase, and dark-field object images. Further, we demonstrate that the method has great potential for real-time quantitative phase imaging and wavefront sensing when combined with deep learning.

Influence of Spatial Losses of the Signal Detected by a Single-Pixel Detector on the Quality of Object Image Reconstruction

Influence of Spatial Losses of the Signal Detected by a Single-Pixel Detector on the Quality of Object Image Reconstruction

Computational ghost imaging based on the conditional adversarial network

A framework of computational ghost imaging based on the conditional adversarial network is proposed to efficiently implement the reconstruction of object images in this research. Although the traditional ghost imaging can recover object images with a few amount of measured intensities and corresponding speckle patterns, the visual quality of reconstructed images is seriously influenced by a lot of noise. To solve this issue, a conditional adversarial network is designed to improve the quality of the noisy images reconstructed by the traditional ghost imaging. The objective function is composed of multiple loss metrics, which avoid suffering from training instability of the network. Especially, the combination of critic loss and gradient penalty metrics guarantees that a wide variety of network architectures are used as the generator and almost no hyperparameter tuning is required in the network. Most importantly, the proposed network can be trained with simulated data without spending a long time to collect the practical training samples and considering the experimental condition. After training, the parameters of the network such as kernel weights of convolution filters are optimized from the adversarial process between the generator and the discriminator, object images can be perfectly reconstructed at a very low sampling ratio. Through the simulated and optical experiments, it is demonstrated that the proposed method has much better performance than other methods such as the traditional and compressive-sensing-based methods.