On-Board Flickering Pixel Dynamic Suppression Method Based on Multi-Feature Fusion

The blind pixel suppression is the key preprocess to guarantee the real-time space-based infrared point target (IRPT) detection and tracking. Meanwhile, flickering pixels, as one of the blind pixels, is hard to suppress because of randomness. At present, common methods adopting a single feature generally need to accumulate dozens or hundreds of frames to ensure detection accuracy, which cannot update flickering pixels frequently. However, with low detection frequency, the flickering pixels are easily miss detected. In this paper, we propose an on-board flickering pixel dynamic suppression method based on multi-feature fusion. The visual and motion features of flickering pixels are extracted from the result of IRPT detection and tracking. Then, the confidence of flickering pixel evaluation strategy and selection mechanism of flickering pixel are introduced to fuse the above features, which achieves accurate flickering pixel suppression using a dozen frames. The experimental results evaluated on the real image of four scenarios show that the blind pixel false detection rate of the proposed method is no more than 1.02%. Meanwhile, evaluated on the simulated image, the flickering pixel miss suppression rate is no more than 2.38%, and the flickering pixel false suppression rate is 0. The proposed method could be an addition to most other IRPT detection methods, which guarantees the near-real-time and reliability of on-board IRPT detection applications.

Spatial and Temporal Resolution Improvement of Actual Evapotranspiration Maps Using Landsat and MODIS Data Fusion

Producing daily actual evapotranspiration (ETa) maps with high spatial resolution has always been a challenge for remote sensing research. This study assessed the feasibility of producing daily ETa maps with a high spatial resolution (30 m) for the sugarcane farmlands of Amir Kabir Sugarcane Agro-industry (Khuzestan, Iran) using three different scenarios. In the first scenario, the reflectance bands of Landsat 8 were predicted from the moderate resolution imaging spectroradiometer (MODIS) imagery using the spatial and temporal adaptive reflectance fusion model (STARFM) algorithm. Also, the thermal bands of Landsat 8 were predicted by the spatiotemporal adaptive data fusion algorithm for temperature mapping (SADFAT). Then, ETa amounts were calculated employing such bands and the surface energy balance algorithm for land (SEBAL). In the second scenario, the input data needed by SEBAL were downscaled using the MODIS images and different methods. Then, using the downscaled data and SEBAL, daily ETa amounts with a spatial resolution of 30 m were calculated. In the third scenario, ETa data acquired by MODIS were downscaled to the scale of Landsat 8. In the second and third scenarios, downscaling of the data was carried out by the ratio, regression, and neural networks methods with two different approaches. In the first approach, the Landsat image on day 1 and the relationship between the two MODIS images on day 1 and the other days were used. In the second approach, the simulated image on the previous day and the relationship between the two consecutive images of MODIS were used. Comparing the simulated ETa amounts with the ETa amounts derived from Landsat 8, the first scenario had the best result with an RMSE (root mean square error) of 0.68 mm day−1. The neural networks method used in the third scenario with the second approach had the worst result with an RMSE of 2.25 mm day−1, which was however a better result than the ETa amounts derived from MODIS with an RMSE of 3.19 mm day−1. The method developed in this study offers an efficient and inexpensive way to produce daily ETa maps with a high spatial resolution. Furthermore, we suggest that STARFM and SADFAT algorithms have acceptable accuracies in the simulation of reflectance and thermal bands of Landsat 8 images for homogeneous areas.

Polarimetric SAR Speckle Filtering Using a Nonlocal Weighted LMMSE Filter

Despeckling is a key preprocessing step for applications using PolSAR data in most cases. In this paper, a technique based on a nonlocal weighted linear minimum mean-squared error (NWLMMSE) filter is proposed for polarimetric synthetic aperture radar (PolSAR) speckle filtering. In the process of filtering a pixel by the LMMSE estimator, the idea of nonlocal means is employed to evaluate the weights of the samples in the estimator, based on the statistical equalities between the neighborhoods of the sample pixels and the processed pixel. The NWLMMSE estimator is then derived. In the preliminary processing, an effective step is taken to preclassify the pixels, aiming at preserving point targets and considering the similarity of the scattering mechanisms between pixels in the subsequent filter. A simulated image and two real-world PolSAR images are used for illustration, and the experiments show that this filter is effective in speckle reduction, while effectively preserving strong point targets, edges, and the polarimetric scattering mechanism.

Zernike Coefficient Prediction Technique for Interference Based on Generation Adversarial Network

In the paper, we propose a novel prediction technique to predict Zernike coefficients from interference fringes based on Generative Adversarial Network (GAN). In general, the task of GAN is image-to-image translation, but we design GAN for image-to-number translation. In the GAN model, the Generator’s input is the interference fringe image, and its output is a mosaic image. Moreover, each piece of the mosaic image links to the number of Zernike coefficients. Root Mean Square Error (RMSE) is our criterion for quantifying the ground truth and prediction coefficients. After training the GAN model, we use two different methods: the formula (ideal images) and optics simulation (simulated images) to estimate the GAN model. As a result, the RMSE is about 0.0182 ± 0.0035λ with the ideal image case and the RMSE is about 0.101 ± 0.0263λ with the simulated image case. Since the outcome in the simulated image case is poor, we use the transfer learning method to improve the RMSE to about 0.0586 ± 0.0035λ. The prediction technique applies not only to the ideal case but also to the actual interferometer. In addition, the novel prediction technique makes predicting Zernike coefficients more accurate than our previous research.

An Artificial Intelligence Tool for Image Simulation in Rhinoplasty

During rhinoplasty consultations, surgeons typically create a computer simulation of the expected result. An artificial intelligence model (AIM) can learn a surgeon's style and criteria and generate the simulation automatically. The objective of this study is to determine if an AIM is capable of imitating a surgeon's criteria to generate simulated images of an aesthetic rhinoplasty surgery. This is a cross-sectional survey study of resident and specialist doctors in otolaryngology conducted in the month of November 2019 during a rhinoplasty conference. Sequential images of rhinoplasty simulations created by a surgeon and by an AIM were shown at random. Participants used a seven-point Likert scale to evaluate their level of agreement with the simulation images they were shown, with 1 indicating total disagreement and 7 total agreement. Ninety-seven of 122 doctors agreed to participate in the survey. The median level of agreement between the participant and the surgeon was 6 (interquartile range or IQR 5–7); between the participant and the AIM it was 5 (IQR 4–6), p-value < 0.0001. The evaluators were in total or partial agreement with the results of the AIM's simulation 68.4% of the time (95% confidence interval or CI 64.9–71.7). They were in total or partial agreement with the surgeon's simulation 77.3% of the time (95% CI 74.2–80.3). An AIM can emulate a surgeon's aesthetic criteria to generate a computer-simulated image of rhinoplasty. This can allow patients to have a realistic approximation of the possible results of a rhinoplasty ahead of an in-person consultation. The level of evidence of the study is 4.

Learned deconvolution using physics priors for structured light-sheet microscopy

Structured propagation-invariant light fields, such as the Airy and Bessel beams, can encode high-resolution spatial information over an extended field of view. Their use in microscopy, however, has been limited due to the need for deconvolution, a challenging inverse problem. Here, we introduce a deep learning method that can deconvolve and super-resolve structured light-sheet images using such fields without the need for paired experimental data. We make use of the known physics of light propagation by constraining a generative adversarial network with estimated, simulated image data. We combine this with unpaired experimental data via a saliency constraint based on perceptual loss. The combined model results in an experimentally unsupervised network that is robust and lightweight, and that can be trained solely on a few regions of interest from one light-sheet volume. We demonstrate its performance on Airy light-sheet volumes of calibration beads, oocytes, preimplantation embryos, and excised brain tissue. This democratises the use of structured light fields and deconvolution, as it does not require data acquisition outwith the conventional imaging protocol.

Standardization of three-dimensional pose of cylindrical implants from intraoral radiographs: a preliminary study

Background To introduce a theoretical solution to a posteriori describe the pose of a cylindrical dental fixture as appearing on radiographs; to experimentally validate the method described. Methods The pose of a conventional dental implant was described by a triplet of angles (phi-pitch, theta-roll, and psi-yaw) which was calculated throughout vector analysis. Radiographic- and simulated-image obtained with an algorithm were compared to test effectiveness, reproducibility, and accuracy of the method. The length of the dental implant as appearing on the simulated image was calculated by the trigonometric function and then compared with real length as it appeared on a two-dimensional radiograph. Results Twenty radiographs were analyzed for the present in silico and retrospective study. Among 40 fittings, 37 resulted as resolved with residuals ≤ 1 mm. Similar results were obtained for radiographic and simulated implants with absolute errors of − 1.1° ± 3.9° for phi; − 0.9° ± 4.1° for theta; 0° ± 1.1° for psi. The real and simulated length of the implants appeared to be heavily correlated. Linear dependence was verified by the results of the robust linear regression: 0.9757 (slope), + 0.1344 mm (intercept), and an adjusted coefficient of determination of 0.9054. Conclusions The method allowed clinicians to calculate, a posteriori, a single real triplet of angles (phi, theta, psi) by analyzing a two-dimensional radiograph and to identify cases where standardization of repeated intraoral radiographies was not achieved. The a posteriori standardization of two-dimensional radiographs could allowed the clinicians to minimize the patient’s exposure to ionizing radiations for the measurement of marginal bone levels around dental implants.

Precise Orbit Determination of MEX Flyby Phobos Using Simulated Radiometric and Image Data

A navigation camera or topography camera is a standard payload for deep space missions and the image data are normally used for auto-navigation. In this work, we study the potential contribution of image data in precise orbit determination for deep space spacecraft. The Mars Express (MEX) spacecraft has generated extensive Phobos image data during flybys of Phobos, but these data have not been used in precise orbit determination because of the difficulty in employing these image data. Therefore, we did an experiment using simulated image data as the first step for exploring how to use real image data in precise orbit determination of spacecraft. Our results demonstrate that image data can provide stronger constraints on orbit in the tangential and normal directions than Doppler data. When the image data were used in the MEX orbit determination during the MEX Phobos flyby, the orbit determination accuracies in the tangential and normal directions were significantly improved. This work will provide a reference for real image data processing during MEX Phobos flyby to improve MEX orbit accuracy as well as Phobos ephemeris accuracy.

Standardization of Three-Dimensional Pose of Cylindrical Implants From Intraoral Radiographs

BackgroundTo introduce a theoretical solution to a posteriori describe the pose of a cylindrical dental fixture as appearing on radiographs; to experimentally validate the method described.MethodsThe pose of a conventional dental implant was described by a triplet of angles (phi-pitch, theta-roll, and psi-yaw) which was calculated throughout vector analysis. Radiographic- and simulated-image obtained with an algorithm were compared to test effectiveness, reproducibility, and accuracy of the method. The length of the dental implant as appearing on the simulated image was calculated by the trigonometric function and then compared with real length as it appeared on a two-dimensional radiograph. ResultsTwenty radiographs were analyzed for the present in silico and retrospective study. Among 40 fittings, 37 resulted as resolved with residuals ≤1mm. Similar results were obtained for radiographic and simulated implants with absolute errors of -1.1±3.9° for phi; -0.9±4.1° for theta; 0±1.1° for psi. The real and simulated length of the implants appeared to be heavily correlated. Linear dependence was verified by the results of the robust linear regression: 0.9757 (slope), +0.1344mm (intercept), and an adjusted coefficient of determination of 0.9054.ConclusionsThe method allowed clinicians to calculate, a posteriori, a single real triplet of angles (phi,theta,psi) by analyzing a two-dimensional radiograph and to identify cases where standardization of repeated intraoral radiographies was not achieved. The a posteriori standardization of two-dimensional radiographs could allowed the clinicians to minimize the patient's exposure to ionizing radiations for the measurement of marginal bone levels around dental implants.Trial registrationThe Human Investigation Committee (IRB) of University of Pisa approved present retrospective data analysis (Ethical Approval Form 2626/2008 Protocol Number 58183)

A Deep Learning Method for Limited-View Intravascular Photoacoustic Image Reconstruction

Intravascular photoacoustic tomography (IVPAT) is a newly developed imaging modality in the interventional diagnosis and treatment of coronary artery diseases. Incomplete acoustic measurement caused by limitedview scanning of the detector in the vascular lumen results in under-sampling artifacts and distortion in the images reconstructed by using the standard reconstruction methods. A method for limited-view IVPAT image reconstruction based on deep learning is presented in this paper. A convolutional neural network (CNN) is constructed and trained with computer-simulated image data set. Then, the trained CNN is used to optimize the cross-sectional images of the vessel which are recovered from the incomplete photoacoustic measurements by using the standard time-reversal (TR) algorithm to obtain the images with the improved quality. Results of numerical demonstration indicate that the method can effectively reduce the image distortion and artifacts caused by the limited-view detection. Furthermore, it is superior to the compressed sensing (CS) method in recovering the unmeasured information of the imaging target with the structural similarity around 10% higher than CS reconstruction.