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The visibility samples generated by the synthetic aperture interferometric radiometer (SAIR) under near-field observation conditions contain information about the distance from the target to the instrument. This requires a precise understanding of the target–instrument distance to guarantee imaging quality in near-field SAIR applications. In this paper, we introduce a novel distance estimate approach for near-field SAIR systems, which achieves satisfactory imaging performance in the absence of prior information on target–instrument distance. First, we reformulate the signal model of near-field SAIR from the fractional Fourier transform (FRFT) perspective. This formulation ties the distance that is variable to the visibility function in a straightforward manner, offering an efficient solution for image reconstruction in near-field SAIR. Subsequently, we present an iterative strategy for target–instrument distance estimation based on simulated annealing (SA). In each iteration, the modified average gradient (MAG) of images reconstructed within the FRFT framework is evaluated, and based on the Metropolis criterion, the estimated target–instrument distance is optimally updated iteratively. Finally, the validity and effectiveness of the proposed distance estimation method for near-field SAIR imaging systems are demonstrated through numerical simulation and real experiments. [ABSTRACT FROM AUTHOR]

The novel rotating synthetic aperture (RSA) is a new optical imaging system that uses the method of rotating the rectangular primary mirror for dynamic imaging. It has the advantage of being lightweight, with no need for splicing and real-time surface shape maintenance on orbit. The novel imaging method leads to complex image quality degradation characteristics. Therefore, it is vital to use the image quality improvement method to restore and improve the image quality to meet the application requirements. For the RSA system, a new system that has not been applied in orbit, it is difficult to construct suitable large datasets. Therefore, it is necessary to study and establish the dynamic imaging characteristic model of the RSA system, and on this basis provide data support for the corresponding image super resolution and restoration method through simulation. In this paper, we first analyze the imaging characteristics and mathematically model the rectangular rotary pupil of the RSA system. On this basis, combined with the analysis of the physical interpretation of the blur kernel, we find that the optimal blur kernel is not the point spread function (PSF) of the imaging system. Therefore, the simulation method of convolving the input image directly with the PSF is flawed. Furthermore, the weights of a convolutional neural network (CNN) are the same for each input. This means that the normal convolutional layer is not only difficult to accurately estimate the time-varying blur kernel, but also difficult to adapt to the change in the length–width ratio of the primary mirror. To that end, we propose a blur kernel estimation conditional convolutional neural network (CCNN) that is equivalent to multiple normal CNNs. We extend the CNN to a conditional model by taking an encoding as an additional input and using conditionally parameterized convolutions instead of normal convolutions. The CCNN can simulate the imaging characteristics of the rectangular pupil with different length–width ratios and different rotation angles in a controllable manner. The results of semi-physical experiments show that the proposed simulation method achieves a satisfactory performance, which can provide data and theoretical support for the image restoration and super-resolution method of the RSA system. [ABSTRACT FROM AUTHOR]

Near-field high-resolution synthetic aperture radar (SAR) imaging is mostly accompanied by a large number of data acquisition processes, which increases the system complexity and device cost. According to extensive reports, reducing the number of sampling points of a radar in space can greatly reduce the amount of data. However, when spatial sparse sampling is carried out, a ghost normally appears in the imaging results due to the high side lobes generated in the azimuth. To address this issue, a technique is introduced in this paper to recover the blank data through amplitude and phase compensation based on the correlation between sparse array sampling through adjacent points. Firstly, the data sampled by the sparse array is compressed in the range direction to obtain the expected data slices in the same range direction. Then, the blank element of the slice is compensated for with amplitude and phase to obtain full aperture data. Finally, the matched filter method is used to aid in the image reconstruction. The simulation results verified that the method proposed in this paper can effectively reconstruct the image under two kinds of sparse sampling conditions. Thus, a simple single-input single-output (SISO) synthetic aperture radar imaging test bench is established. Compared with the results of a 1 mm (1/4 λ) sampling interval, the quality of the reconstructed image under the condition of a 4 mm (1 λ) sampling interval still stands using our proposed method. Demonstrated by the experiment, the normalized root-mean-square error(NMSE) is 5.75%. Additionally, when the spatial sampling points are sampled randomly with 30% of the full sampling condition, this method can also restore and reconstruct the image with high quality. Due to the decrease of sampling points, the data volume can be reduced, which is beneficial for improving the scanning speed and alleviating the pressure of data transmission for near-field high resolution SAR imaging systems. [ABSTRACT FROM AUTHOR]

A novel super-resolution volumetric photoacoustic microscopy, based on the theory of structured-illumination, is proposed in this paper. The structured-illumination will be introduced in order to surpass the diffraction limit in a photoacoustic microscopy (PAM) structure. Through optical excitation of the targeted object with a sinusoidal spatial fringe pattern, the object’s frequency spectrum is forced to shift in the spatial frequency domain. The shifting in the desired direction leads to the passage of the high-frequency contents of the object through the passband of the acoustic diffraction frequency response. Finally, combining the low-frequency image with the high-frequency parts in four regular orientations in the spatial frequency domain is equivalent to imaging the targeted object with an imaging system of two-fold bandwidth and thus half lateral resolution. In order to obtain the image of out-of-focus regions and improve the lateral resolution outside the focal region of a PAM imaging system, Fourier-domain reconstruction algorithm based on the synthetic aperture focusing technique (SAFT) using the virtual detector concept is utilized for reduction in the required computational load and time. The performance of the proposed imaging system is validated with in vivo and ex vivo targets. The experimental results obtained from several tungsten filaments in the depth range of 1.2 mm, show an improvement of −6 dB lateral resolution from 55–287 μm to 25–29 μm and also an improvement of signal-to-noise ratio (SNR) from 16–22 dB to 27–33 dB in the proposed system. [ABSTRACT FROM AUTHOR]

In order to improve the image quality of a coded-aperture-based gamma-ray imaging apparatus, a method for removing Compton scattered and other multipixel events is presented that removes inaccurate position estimations from the data stream. In coded-aperture gamma-ray imaging detectors, scattering events that are shared between different partitions in the active medium can result in the inaccurate determination of the initial gamma-ray interaction position, the result of which is distortion in the angular reconstruction mapping. For coded-aperture system in which the rapid localization and identification of the emitting source is important, the distortion due to scattered radiation can increase the integration time of the system. We show, in both software and hardware implementations using a cerium-doped gadolinium aluminum gallium garnet pixelated scintillator array, that filtering the data to remove multipixel events can improve the image quality despite the efficiency loss associated with not utilizing all of the interactions within the detector. [ABSTRACT FROM AUTHOR]

A 3-D synthetic transmit aperture ultrasound imaging system with a fully addressed array usually leads to high hardware complexity and cost since each element in the array is individually controlled. To reduce the hardware complexity, we had presented the large-pitch synthetic transmit aperture (LPSTA) ultrasound imaging for 2-D imaging using a 1-D phased array to reduce the number of measurement channels M (the product of number of transmissions, IT, and the number of receiving channels in each transmission, IR). In this article, we extend this method to a 2-D matrix array for 3-D imaging. We present both numerical simulations and experimental measurements. We combined L × L adjacent elements into transmission subapertures (SAP) and K × K adjacent elements into receive SAPs in synthetic transmit aperture (STA) imaging. In the image reconstruction, we conducted the first attempt to apply and integrate Gaussian-approximated spatial response function (G-SRF) with delay and sum (DAS) to improve the image contrast, especially for the near-field targets. The imaging performance obtained from G-SRF was also evaluated numerically and compared with the previously presented frequency-domain SRF (Freq-domain SRF). The 3-D large-pitch synthetic transmit aperture (3-D-LPSTA) with G-SRF can provide a computationally efficient solution compared with the standard 3-D-STA method. With approximately 1900-fold reduction in the number of measurement channels, 3-D-LPSTA can provide image contrast comparable with the standard 3-D-STA with a full array and significantly better than using a periodically sparse array with similar complexity. In addition to reducing the system complexity, the 3-D-LPSTA achieves 700-fold reduction in computational complexity and 523-fold reduction in data storage. Finally, we evaluated and implemented the G-SRF using phantom data, which were consistent with the simulation results showing that the G-SRF can improve the image contrast. The results demonstrate that the proposed 3-D-LPSTA shows the great potential for designing an inexpensive ultrasound system to ensure the real-time 3-D clinical ultrasound imaging using large arrays. The limits of the proposed method were also discussed. [ABSTRACT FROM AUTHOR]

Synthetic aperture imaging systems can be applied to equivalently get high-resolution images of traditional monolithic primary mirror systems with less weight and costs. Spatial modulation diversity (SMD), a newly developed post-processing technology applicable for such synthetic aperture systems, is very sensitive to Gaussian noise, which greatly limits its further application. In this paper, we propose an improved SMD strategy by introducing the deep denoising convolutional neural networks (DnCNN) into the image preprocessing to improve the robustness of SMD. Results of the numerical simulations demonstrate that the strategy proposed exhibits superior performance compared to the traditional SMD technique in terms of both the root-mean-square error (RMSE) of phase estimates and the structural similarity (SSIM) of image reconstruction. In view of the superiority and robustness, the method we proposed may have important application prospects in multi-aperture imaging systems. [ABSTRACT FROM AUTHOR]

Multistatic millimeter-wave imaging structures are superior to their monostatic counterparts for imaging natural objects of sudden profile variations. Multistatic image reconstruction is conventionally performed via synthetic aperture radar (SAR)-based methods which, in spite of their high accuracy, are computationally burdensome. On the other side, the Fourier-based image reconstruction in multistatic systems also faces few challenges including multidimensional interpolation, a plane-wave approximation of spherical waves, and $k$ -space partitioning. This paper presents a fast implementation of the SAR-based image reconstruction method in case of 1-D and 2-D multistatic arrays. The proposed implementation is Fourier based across the uniform direction in $k$ -space and SAR based along the nonuniform direction. Both methods are fully parallelizable and are arranged into a vector format to maximize memory usage and minimize the computational time. The extensive validation and benchmarking have been performed with both simulation and experimental data, which proves a $256\times $ improvement in the reconstruction time for the worst case scenario compared to that of in SAR-based methods with the same image quality. Furthermore, the reconstructed image performance is around $10\times $ better than the most recent Fourier-based reconstruction method, in terms of root-mean-square error metric, and with $28\times $ less computational time. [ABSTRACT FROM AUTHOR]

The resolution of an optical imaging system is often limited by various phase aberrations. To get the joint estimation of an object and aberrations of an imaging system with perturbations, a defocused spatial diversity technology with aperture scanning was proposed and evaluated. This technology creates spatial diversity images by scanning an aperture in the defocused plane of the aberrated imaging system. Based on these diversity images, the stochastic parallel gradient descent algorithm was used to recover the phase aberration of the imaging system by adaptively optimizing the coefficients of Zernike polynomials. Then the near-diffraction-limited image of the object can be restored using a multiframe Winner-Helstrom filter. Numerical simulations performed with different phase distributions validated the technology. The technology proposed may be widely used in aberrated imaging systems for aberration detection and image restoration. [ABSTRACT FROM AUTHOR]

This paper describes a way to synthesize a larger coherent aperture from smaller apertures combined with motion, when only intensities are measured. It relies on collecting intensity patterns in two planes for each aperture, for example, the aperture plane and an image plane, and using a phase-retrieval algorithm to reconstruct the optical field in the aperture plane. As the sensor moves forward, a larger two-dimensional aperture is synthesized, allowing a much finer resolution image to be reconstructed. An algorithm to correct for the relative pointing (tip and tilt phases) and piston errors between different apertures and at different times is needed to phase up the synthetic aperture. Results of simulations, including the effects of speckle, are shown, and practical considerations are evaluated. [ABSTRACT FROM AUTHOR]

Based on photonic integrated circuit (PIC) technology and synthetic aperture technology, the advanced technology of segmented planar imaging, which can be used to realize ultrathin and high-resolution imaging, is proposed. We develop a sampling lens array structure that combines coherent detection and traditional imaging; it can improve the sampling of low- and medium-frequency information. Based on the proposed lens array structure, a full-chain simulation model of the segmented planar imaging system is established. Under the premise of similar imaging quality, compared with the existing sampling lens structures, the proposed lens array has a simpler structure, lower processing difficulty, and lower cost. The simulation analyses of the duty ratio of the lens array, the light splitting number of PIC arrayed waveguide gratings, and the radius of the single lens are carried out. The simulation results provide a certain reference value for the optimal design of the segmented planar optical imaging system and further prove the advantages of the proposed structure. [ABSTRACT FROM AUTHOR]

Abstract: Fast decoding algorithms are described for a number of established coded aperture systems. The fast decoding algorithms for all these systems offer significant reductions in the number of calculations required when reconstructing images formed by a coded aperture system and hence require less computation time to produce the images. The algorithms may therefore be of use in applications that require fast image reconstruction, such as near real-time nuclear medicine and location of hazardous radioactive spillage. Experimental tests confirm the efficacy of the fast decoding techniques. [Copyright &y& Elsevier]

The blur is a problem impacting the image quality of optical synthetic aperture imaging (OSAI) system and requiring image restoration techniques for deconvolution. However, most traditional image deconvolution algorithms are non-blind methods and also highly susceptible to the unknown noise. The turbulence perturbation and the co-phase error are both factors that may affect the recovery performance. As for the image restoration under noises and turbulence correction error, a further optimized network named as RestoreNet-Plus is designed. After further optimization, RestoreNet-Plus has advantages as below: • Blind restoration ability and generalization capacity: RestoreNet-Plus could remove the blur better in the blind way facing different targets through OSAI system; • Anti-noise ability: It could beat other methods under different levels noises, including Wiener filter, Hyper-Laplacian prior algorithm, Lucy-Richardson algorithm, Blind Deconvolution algorithm and RestoreNet. • Anti-turbulence correction error ability: RestoreNet-Plus could maintain good Blind restoration ability under the influence of turbulence correction error in imaging. • Fast for applying: RestoreNet-Plus performances the best in the image restoration of OSAI system under noises and turbulence correction error with only 54 ms time costs. The synthetic aperture technology can improve the resolution effectively in the optical imaging system. In fact, the imaging blur, turbulence aberration and noise can affect the imaging quality of optical synthetic aperture imaging system seriously. Several non-blind methods are applied generally to recover the degraded maps with the prior information. However, the restoration effect is not stable enough and satisfactory. As a data-driven approach, the deep learning framework possesses advantages in solving this problem. In this paper we propose an improved network, RestoreNet-Plus, for the image restoration of optical synthetic aperture imaging system. After the proofs of numerical simulation and experiment results, RestoreNet-Plus is a better alternative compared with other methods, owing to its better restoration ability, strong denoising ability and capacity for turbulence correction error. [ABSTRACT FROM AUTHOR]

The number of publications of aperture-synthesis images based on optical long-baseline interferometry measurements has recently increased due to easier access to visible and infrared interferometers. The interferometry technique has now reached a technical maturity level that opens new avenues for numerous astrophysical topics requiring milli-arcsecond model-independent imaging. In writing this paper our motivation was twofold: (1) review and publicize emblematic excerpts of the impressive corpus accumulated in the field of optical interferometry image reconstruction; (2) discuss future prospects for this technique by selecting four representative astrophysical science cases in order to review the potential benefits of using optical long-baseline interferometers. For this second goal we have simulated interferometric data from those selected astrophysical environments and used state-of-the-art codes to provide the reconstructed images that are reachable with current or soon-to-be facilities. The image-reconstruction process was 'blind' in the sense that reconstructors had no knowledge of the input brightness distributions. We discuss the impact of optical interferometry in those four astrophysical fields. We show that image-reconstruction software successfully provides accurate morphological information on a variety of astrophysical topics and review the current strengths and weaknesses of such reconstructions. We investigate how to improve image reconstruction and the quality of the image possibly by upgrading the current facilities. We finally argue that optical interferometers and their corresponding instrumentation, existing or to come, with six to ten telescopes, should be well suited to provide images of complex sceneries. [ABSTRACT FROM AUTHOR]

A coded imaging and decoding (image reconstruction) scheme was developed for diagnosing a hot and dense region emitting hard x-rays and neutrons in laser-fusion plasmas. Because the imager was a uniformly redundant array of penumbral aperture (URPA) arranged in an M-matrix, URPA leads to N times (N: the total number of apertures) enhancement of signal intensity in comparison with a single penumbral aperture. A recorded penumbral image was reconstructed by a computer-based heuristic method to reduce artifacts caused by noises contained in a penumbral image. Applicability of this technique was investigated by imaging x-rays emitted from laser-produced plasmas, demonstrating a spatial resolution of 16 μm. Under the present conditions, the spatial resolution was determined dominantly by a detector resolution (10.5 μm) and a signal-to-noise ratio of the obtained penumbral image. [ABSTRACT FROM AUTHOR]

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission aims at producing global and frequent maps of SMOS and will be launched in 2008. SMOS' single payload is a new type of radiometer called Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) operating at L-band in which brightness temperature images are formed by a Fourier synthesis technique. However, in the alias-free field of view where the brightness temperature images are reconstructed, a bias is present which has been found to be higher for high- contrast brightness temperature scenes (coastlines) and lower for homogeneous scenes (all oceans or lands). This scene-dependent bias will ultimately limit the achievable accuracy of the retrieved geophysical parameters, and it is particularly critical for the retrieval of sea surface salinity. This paper presents a general analysis of the origin of this bias, which is found to be actually due to the different measurement errors in the instrument observables (visibility samples). An improvement of the image reconstruction algorithm is then presented to mitigate it. As compared with the previous algorithm versions, the proposed improved reconstruction algorithm further decomposes the visibility samples into some new terms: ocean and land/iced sea, instead of just the Earth's disk over the sky background. This decomposition aims at further reducing the contrast (high-frequency components) in the differential image and, therefore, minimizes the impact of multiplicative errors, improving the radiometric accuracy. In addition, this approach proves to perform the image reconstruction in part of the alias regions and improves the quality of the reconstruction close to the coastlines. [ABSTRACT FROM AUTHOR]

We propose a new quantization method for received synthetic aperture radar (SAR) signals and a quantizer design technique. This method divides the received signal into bandlimited signals by using orthogonal wavelet transforms and quantizes each bandlimited signal by a different quantizer. Employing this method, not only is there a reduction in the computational load for image reconstruction by the SAR fast approximate reconstruction proposed previously by the authors, but the accuracy of the reconstructed image is substantially improved when there are few quantization bits of the received signal, particularly in image reconstruction by the correlation method. In this paper, we design a quantizer for bandlimited signals and demonstrate the effectiveness of the proposed method by computer simulations of image reconstruction. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 87(9): 54–63, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecja.10182 [ABSTRACT FROM AUTHOR]

• Non-blind algorithms, such as the Wiener filter and the Lucy-Richardson algorithm, are always used to restore images of the optical synthetic aperture imaging (OSAI) system, but can't apply in practice easily due to the unknown prior information of system. Deep learning is a new technology rising nowadays, and has many successful application cases in different fields. An improved U-shaped network named RestoreNet is designed to remove the blur and recover the image from OSAI system. • After enough training, RestoreNet has advantages as below: • Restoring ability : After enough training, RestoreNet can remove the blur definitely and easily in OSAI system; • Fast for applying : In testing period, the recovering time for one image is just ~30ms , but the restoration effect is better than the Lucy-Richardson algorithm, which takes 470ms for one image; • Prior information unknown : RestoreNet can restore images without the prior information of system , for example, the point spread function or the optical transfer function, which is not provided by the non-blind algorithms; • Generalization ability : RestoreNet has strong ability in generalization ability whether facing different types of images or imaging systems with different sub-aperture arrangements. Imaging blur is an inevitable problem in optical synthetic aperture imaging system because of the low response of frequency. Non-blind deconvolution algorithms are usually used for image restoration to obtain clear and high-resolution images. However, accurate prior information on the optical transfer function of system is required in the non-blind methods. As a data-driven approach, recent developments in deep learning have shown great potential in image processing. In this paper, a U-shaped deep learning framework named RestoreNet is proposed for image restoration, especially for removing the blur of optical synthetic aperture imaging system in a blind way. Numerical simulation and experiment results show that RestoreNet is an effective alternative with great restoration ability, stability and generalization in the image restoration of optical synthetic aperture imaging system. [ABSTRACT FROM AUTHOR]