Reconstruction Of Penumbral Images Research Articles

Quantification and visualization of uncertainties in reconstructed penumbral images of implosions at Omega.

Penumbral imaging is a technique used in plasma diagnostics in which a radiation source shines through one or more large apertures onto a detector. To interpret a penumbral image, one must reconstruct it to recover the original source. The inferred source always has some error due to noise in the image and uncertainty in the instrument geometry. Interpreting the inferred source thus requires quantification of that inference's uncertainty. Markov chain Monte Carlo algorithms have been used to quantify uncertainty for similar problems but have never been used for the inference of the shape of an image. Because of this, there are no commonly accepted ways of visualizing uncertainty in two-dimensional data. This paper demonstrates the application of the Hamiltonian Monte Carlo algorithm to the reconstruction of penumbral images of fusion implosions and presents ways to visualize the uncertainty in the reconstructed source. This methodology enables more rigorous analysis of penumbral images than has been done in the past.

Neutron penumbral image reconstruction with a convolution neural network using fast Fourier transform.

In Inertial Confinement Fusion (ICF), the asymmetry of a hot spot is an important influence factor in implosion performance. Neutron penumbral imaging, which serves as an encoded-aperture imaging technique, is one of the most important diagnostic methods for detecting the shape of a hot spot. The detector image is a uniformly bright range surrounded by a penumbral area, which presents the strength distribution of hot spots. The present diagnostic modality employs an indirect imaging technique, necessitating the reconstruction process to be a pivotal aspect of the imaging protocol. The accuracy of imaging and the applicable range are significantly influenced by the reconstruction algorithm employed. We develop a neural network named Fast Fourier transform Neural Network (FFTNN) to reconstruct two-dimensional neutron emission images from the penumbral area of the detector images. The FFTNN architecture consists of 16 layers that include a FFT layer, convolution layer, fully connected layer, dropout layer, and reshape layer. Due to the limitations in experimental data, we propose a phenomenological method for describing hot spots to generate datasets for training neural networks. The reconstruction performance of the trained FFTNN is better than that of the traditional Wiener filtering and Lucy-Richardson algorithm on the simulated dataset, especially when the noise level is high as indicated by the evaluation metrics, such as mean squared error and structure similar index measure. This proposed neural network provides a new perspective, paving the way for integrating neutron imaging diagnosis into ICF.

A new nonlinear reconstruction method based on total variation regularization of neutron penumbral imaging

Neutron penumbral imaging is a significant diagnostic technique in laser-driven inertial confinement fusion experiment. It is very important to develop a new reconstruction method to improve the resolution of neutron penumbral imaging. A new nonlinear reconstruction method based on total variation (TV) regularization is proposed in this paper. A TV-norm is used as regularized term to construct a smoothing functional for penumbral image reconstruction in the new method, in this way, the problem of penumbral image reconstruction is transformed to the problem of a functional minimization. In addition, a fixed point iteration scheme is introduced to solve the problem of functional minimization. The numerical experimental results show that, compared to linear reconstruction method based on Wiener filter, the TV regularized nonlinear reconstruction method is beneficial to improve the quality of reconstructed image with better performance of noise smoothing and edge preserving. Meanwhile, it can also obtain the spatial resolution with 5 μm which is higher than the Wiener method.

Fast and Robust Reconstruction of Penumbral Images by Combining Multiple Wiener Filters

Penumbral imaging is a powerful technique for imaging with penetrating radiation such as neutrons and hard x rays. The reconstructed image can be obtained by the deconvolution of the detected image. Usually a Wiener filter is used for the reconstruction. The limitation of the Wiener filter is that it will introduce a significant distortion when the detected image contains noise. On the other hand, heuristic methods can obtain clear reconstructed image from noisy penumbral images. However, it significantly takes computation time. We proposed a new reconstruction method in the penumbral imaging to obtain clear reconstructed image by combining the multiple Wiener filters and the method can reduce computation time. The effectiveness of the proposed method has been demonstrated by computer simulation and real laser plasma experiment.

Obtaining point spread function of penumbral encoding aperture with “expectation maximization” algorithm based on matched source-image pair experiment

A source penumbral image reconstruction method with linear mapping principle for geometrical optics is established. The ideal binary point spread function (PSF) can be obtained using a geometrical optics model. The system PSF with certain sharpness was obtained using a Monte Carlo (MC) model. Considering other factors besides the transportation of the x (gamma)-rays or particles (fusion neutrons) in the penumbral encoding aperture in MC model, such as the scattering background and the systematic error, the PSF from MC model "source-image pair matching" experiments with a large area standard oval shape source were processed. A method for correcting and calibrating the PSF by the expectation maximization adaptive algorithm was established and the optimized PSF with 22.30 microm sharpness was achieved. This is more consistent with the real system PSF despite the increased noise level of the two-dimensional PSF and large irregularity in the PSF profile.

Heuristic reconstructions of neutron penumbral images

Penumbral imaging is a technique of coded aperture imaging proposed for imaging of highly penetrating radiations. To date, the penumbral imaging technique has been successfully applied to neutron imaging in laser fusion experiments. Since the reconstruction of penumbral images is based on linear deconvolution methods, such as inverse filter and Wiener filer, the point spread function of apertures should be space invariant; it is also sensitive to the noise contained in penumbral images. In this article, we propose a new heuristic reconstruction method for neutron penumbral imaging, which can be used for a space-variant imaging system and is also very tolerant to the noise.

Temporal resolved x-ray penumbral imaging technique using heuristic image reconstruction procedure and wide dynamic range x-ray streak camera

Temporal resolved x-ray penumbral imaging has been developed using an image reconstruction procedure of the heuristic method and a wide dynamic range x-ray streak camera (XSC). Reconstruction procedure of the penumbral imaging is inherently intolerant to noise, a reconstructed image is strongly distorted by artifacts caused by noise in a penumbral image. Statistical fluctuation in the number of detected photon is the dominant source of noise in an x-ray image, however acceptable brightness of an image is limited by dynamic range of an XSC. The wide dynamic range XSC was used to obtain penumbral images bright enough to be reconstructed. Additionally, the heuristic method was introduced in the penumbral image reconstruction procedure. Distortion of reconstructed images is sufficiently suppressed by these improvements. Density profiles of laser driven brominated plastic and tin plasma were measured with this technique.

A heuristic penumbral imaging technique for measurements of laser-produced plasma density profile

Penumbral imaging is a powerful technique for imaging of penetrating radiations such as neutrons and hard x ray with a high spatial resolution. Recently, the technique has also been used for measurement of laser-produced plasma density profile. The limitation is that the straightforward image or signal reconstruction will introduce a significant distortion if the penumbral image is distorted by the noise. In this article, we propose a heuristic method for reconstruction of penumbral images. Both simulation and experimental results demonstrated that the proposed method is robust and tolerant to the noise.

Distortion-free reconstruction of neutron penumbral images by a genetic algorithm

Penumbral imaging is a technique for imaging of neutrons or other penetrating radiations. The technique uses the facts that spatial information can be recovered from the shadow or penumbra that an unknown source casts through a simple large circular aperture. The limitation is that the straightforward image reconstruction will introduce some significant distortion for a large field of view because of non-isoplanaticity of the aperture point spread function. A genetic algorithm (GA) is proposed for reconstruction of penumbral images, and. The technique allows distortion-free reconstruction over a large field of view. Furthermore, because in GA the complicated a priori constraints can be easily incorporated by the appropriate modification of the cost function, the algorithm is also tolerant of the noise.

Genetic algorithms applied to neutron penumbral imaging

Penumbral imaging is a technique for imaging of neutrons or other penetrating radiations. The technique uses the fact that spatial information can be recovered from the shadow or penumbra that an unknown source casts of a simple large circular aperture. The limitation is that the straightforward image reconstruction will introduce some significant distortion for a large field of view because of non-isoplanaticity of the aperture point spread function. In this paper, a genetic algorithm (GA) is proposed for reconstruction of penumbral images and the technique allows distortion-free reconstruction over a large field of view. Furthermore, because in GA the complicateda priori constraints can be easily incorporated by appropriate modification of the cost function, the algorithm is also very tolerant of the noise.