Error analysis and reconstruction for diffractive optic imaging spectrometer using the multiple iterations

When the degradation is mainly caused by the camera linear motion, or the out of focus lens of the imaging systems, the model of the degradation functions or known as the point spread function (PSF) can be established with the corresponding parameters. In the situations, the identification of the PSF can be converted to the identification of the parameter of the degradation functions. In order to increase the identification accuracy of the degradation function and the computational efficiency in the identification process, the low pass finite impulse response (FIR) filter which is named as the inverse adaptive amplitude spectrum filter (IAASF) proposed in this paper is applied to identify the PSF. First, the amplitude spectra of the degraded images are calculated with discrete fast Fourier transform (DFFT). Second, filtering the amplitude spectra with IAASF in order to accurately measure the distance of the characteristic curves on the filtered amplitude spectra image frames, i.e. measuring the distance of the central two spectra lines on amplitude spectra image frames for the linear motion blur, and measuring the diameter of the inner circle of the concentric circles on the amplitude spectra image frames for the defocus blur, thus, the parameter of the PSF can be determined. Therefore, the PSF can be obtained from the parametric model of degradation function. The original images have been restored by the identified degradation function and the degraded image with Wiener filtering. The experimental results show that the algorithm proposed in this paper has higher computational efficiency and the higher estimation accuracy of the degradation function while the degraded images have higher signal to noise ratio (SNR). The algorithm has certain reference significance for the restoration and identification of the turbulence degraded images yielded by the aero-optical effects.

ISSN:0035-8711

Digital beamforming in array-based UWB radar delivers a high-resolution 3-D image of subsurface in GPR landmine detection while simultaneous data acquisition by elements of the array significantly increases the scanning speed. Such a GPR system with a single transmit antenna and a linear receive array has been developed in the Delft University of Technology. For online processing we propose an advanced imaging algorithm based on migration by regularized, parametric space–time deconvolution. The algorithm deconvolves a 3-D space–time array point spread function out of the data volume by means of FFT and inverse Wiener filter that is being controlled automatically with numerical criteria for stability and accuracy. The prior knowledge of GPR impulse response and ground impulse response is used to form a separate point spread function for each receiving antenna. The developed technique has been verified on experimental data for typical anti-personnel mines and compared with a classical migration by diffraction stacking.

The point spread function (PSF) of a gamma camera describes the photon count density distribution at the detector surface when a point source is imaged. Knowledge of the PSF is important for computer simulation and accurate image reconstruction of single photon emission computed tomography (SPECT) images. To reduce the number of measurements required for PSF characterization and the amount of computer memory to store PSF tables, and to enable generalization of the PSF to different collimator-to-source distances, the PSF may be modeled as the two-dimensional (2D) convolution of the depth-dependent component which is free of detector blurring (PSF(ideal)) and the distance-dependent detector response. Owing to limitations imposed by the radioactive strength of point sources, extended sources have to be used for measurements. Therefore, if PSF(ideal) is estimated from measured responses, corrections have to be made for both the detector blurring and for the extent of the source. In this paper, an approach based on maximum likelihood expectation-maximization (ML-EM) is used to estimate PSF(ideal). In addition, a practical measurement procedure which avoids problems associated with commonly used line-source measurements is proposed. To decrease noise and to prevent nonphysical solutions, shape constraints are applied during the estimation of PSF(ideal). The estimates are generalized to depths other than those which have been measured and are incorporated in a SPECT simulator. The method is validated for Tc-99m and T1-201 by means of measurements on physical phantoms. The corrected responses have the desired shapes and simulated responses closely resemble measured responses. The proposed methodology may, consequently, serve as a basis for accurate three-dimensional (3D) SPECT reconstruction.

Turbulence conditions affect video images in tow ways. It causes local blur, and it distorts the geometry of the scene. A video sequence of a still scene recorded under turbulence, appears to contain local random motion of small neighborhoods in the images. The blur is an accumulated results of the imaging point spread function and the local motion. The geometric distortion is due to the fact that small neighborhoods move in different directions. The restoration scheme reported here takes care of the geometric distortion as well as the blur. The geometric distortion is reduced by averaging the gray levels of relatively long video segments. The averaging reduces the geometric distortion, but it increases the blur. The second stage is the estimation of the global point spread function. The blur in the average image is a combination of the effects of the imaging system transfer function,the turbulence, and the averaging of the sequence. The global non-isotropic point spread function is estimated based on edge responses in the average image. A Wiener filter is used for the restoration of the image. The presented experimental result are superior to the results obtained by a previously proposed majority-vote technique.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Images measured through the atmosphere are degraded by scattering and absorption from aerosols along the path and by atmospheric turbulence. In the presence of heavy scattering at visible and infrared wavelengths, the distances over which reasonable observations are possible are quite short compared to astronomical imaging paradigms, and aerosol scattering effects dominate the degradation of the point spread function (PSF) due to atmospheric effects. In addition to aerosol-induced blurring, measurement noise effects are present in observed images. We examine the problem of reconstructing images degraded by aerosol blur and measurement noise using estimates of the overall PSF, which account for unscattered and scattered radiation detected by the imaging system. Representative images of a spoke target acquired under various conditions of scattering and photon flux levels were simulated, and reconstruction of the degraded images is performed using two linear reconstruction algorithms: a Wiener filter and a constrained least squares filter. Results of the reconstructions show that spatial resolution can be recovered in badly blurred images up to the limit imposed by the noise effective cutoff spatial frequency of the measurement.

In the processing diagnosis of the inertia confinement fusion (ICF), the ring coded aperture imaging technique is applied in order to gain high space and time resolutions simultaneously. The key of acquiring high spatial resolution is how to obtain a point spread function of an imaging system. The common method is the firsthand projection method which is an approximate one. The x-ray diffraction effect is neglected in the method, so the resolution of reconstructed image is decreased. We have derived the point spread function (PSF) of the ring coded aperture from scalar diffraction theory. And the Wiener filter is fabricated on the basis of the PSF. In National Key Laboratory of Laser Fusion, at China Academy of Engineering Physics, the imaging experiment on the diagnosis of ICF is completed using the ring coded aperture plate with inner diameter <i>d</i>₁ = 250&mu;m and outer diameter <i>d</i>₂ = 260&mu;m .The obtained coded image is processed by the Wiener filter which diffractive effect is considered. The processed results have shown that resolution and modulation contrast of acquired image are evidently better than the results obtained by the firsthand projection method.

EPID images are related to the fluence by a (possibly position‐dependent) Point Spread Function (PSF), or kernel. So far, work concerning finding the PSF for a certain EPID has relied on continuous representations. Since the EPID image itself corresponds to a discrete pixel grid, an interesting problem is to find a discrete PSF and do so based only on measurements, without resorting to Monte Carlo simulations. The present work concerns a method for finding such a discrete kernel for the simple case when the kernel is translation‐ and rotation‐invariant. The method relies on minimizing the difference, in a least‐squares sense, between the true acquired image and the image obtained with the calculated kernel. The method was tested using a simulated fluence and EPID image corresponding to a uniform rectangular field. The EPID image was generated by convolving the fluence with a known PSF. The developed algorithm was then tested by comparing the PSF shape produced by the algorithm (based on the image and fluence) with the actual PSF shape used to construct the EPID image. The two were found to match perfectly. A second test was performed by introducing a random error, uniformly distributed in the (−10%, +10%) interval, into the fluence used as input for the algorithm and then reconstructing the EPID image using the calculated PSF. The difference between the “true” EPID image and the reconstructed one was found to be smaller than 0.1% for the simulated field that was studied.

It is important to measure the spatial performance of an in-flight satellite sensor to determine if any spatial resolution degradations occur over time; then some compensation methods can be adopted to improve the image quality. In satellite on-orbit measurements of spatial resolution, the performance of the sensors can be objectively evaluated through the point spread function (PSF) or modulation transfer function (MTF). The well-known image-based PSF derivation method of sampling along a slanted linear step structure from the degraded image is widely used in the satellite image restoration area. Due to the non-uniformity of the linear ground objects, generally, the operators have to pick out good profiles from all the profiles along the edge in order to increase the estimation accuracy. However, only some qualitative criteria have been proposed in the literature to instruct researchers how to pick out good profiles. In this article, we propose a method to quantitatively measure the homogeneity and contrast of the candidate linear structures in order to extract the optimal profiles. This method first automatically detects and locates the slanted linear step edge object in the image. Then it measures the homogeneity and the contrast at each location along the edge by using two adjacent square regions on both sides of the linear step structure. The locations that have high measurement values are regarded as the optimal locations. Finally, the optimal profiles can be extracted at these edge locations to derive an accurate PSF estimation by differentiation. This method makes an automatic optimal step edge detection method for PSF derivation to be practical. Experiment results on simulated and real images are provided. This article shows that using only the ‘optimal’ profiles can play a real and important role and can achieve a good PSF estimation result.

The single-pass modulation transfer function (MTF(sgl)) is an important numerical parameter that can help elucidate the performance and some processes of the human visual system. In previous studies, the MTF(sgl) was calculated from double-pass point spread function (PSF) measurements. These measurements include a depolarized reflection component from the retina that introduces a measurement artifact, and they require long acquisition times to allow averaging to reduce speckle. To solve these problems, we developed a new ocular PSF analysis system (PSFAS) that uses polarization optics to eliminate the depolarized retinal reflection component, and a rotating prism to increase measurement speed. Validation experiments on one patient showed that the MTF(sgl) measured by PSFAS agrees closely with the MTF calculated from contrast sensitivity measurements. A simulated retinal image was calculated by convolution of Landolt rings with the calculated single-pass PSF provided by the PSFAS. The contrast characteristic then was calculated from the simulated retinal images. These results indicate that the MTF(sgl) obtained using the PSFAS may be a reliable measure of visual performance of the optics of the eye, including the optical effects of the retina. The simulated retinal images and contrast characteristics are useful for evaluating visual performance.

This paper addresses the design of an H/sub /spl infin// deconvolution filter and its application to image restoration. The proposed H/sub /spl infin// deconvolution filter has some advantages for image restoration in its capability of handling unknown boundary problems and spatially varying blurs. In this paper, the H/sub /spl infin// filter is compared with the inverse Wiener filter and a regularized restoration method. The experimental results show that the H/sub /spl infin// filter deals with the unknown boundary problem better than the Wiener filter. Compared with the regularization method, it gives a sharper restored image, especially, when the original image contains many details.

Shannon numbers of diffraction images

In lens-based display systems, lens aberrations and depth of field (DoF) limitation often lead to blurring and distortion of reconstructed images; Meanwhile, expanding the display DoF will face a trade-off between horizontal resolution and axial resolution, restricting the achievement of high-resolution and large DoF three-dimensional (3D) displays. To overcome these constraints and enhance the DoF and resolution of reconstructed scenes, we propose a DoF expansion method based on diffractive optical element (DOE) optimization and image pre-correction through a convolutional neural network (CNN). This method applies DOE instead of the conventional lens and optimizes DOE phase distribution using the Adam algorithm, achieving depth-invariant and concentrated point spread function (PSF) distribution throughout the entire DoF range; Simultaneously, we utilize a CNN to pre-correct the original images and compensate for the image quality reduction introduced by the DOE. The proposed method is applied to a practical integral imaging system, we effectively extend the DoF of the DOE to 400 mm, leading to a high-resolution 3D display in multiple depth planes. To validate the effectiveness and practicality of the proposed method, we conduct numerical simulations and optical experiments.

To investigate the degrading effects of the physical parameters on the in-line X-ray phase-contrast imaging (XPCi), a simulation tool based on the Fresnel/Kirchhoff diffraction integral was firstly developed with comprehensively considering effects of the source-to-sample (S-S) and sample-to-detector (S-D) distances, the practical characteristics of a polychromatic and finite size source, the point spread function (PSF) of the fluorescent screen and the spatial resolution of the detector on the theoretical phase-contrast pattern. By a comparison between the simulative profile and the experimental one under the commonly-used parameters, an acceptable consistency has been demonstrated in despite of the deviation between the theoretically-predicted contrast (0.188) and the original experimental one (0.12). From the simulations, it is apparently observed that the fine interference pattern has been severely degraded by the finite spatial resolution, and will inevitably be further deteriorated by the system noise in practice. Since the image quality of the X-ray phase-contrast imaging is strongly dependent on the physical parameters of the system, a model-based deblurring procedure to upgrade the image visibility is preferably desired. As a simple restoration way, a Wiener filter was then introduced to offer an optimal tradeoff between the contrast preservation and the noise suppression. Finally, to minimize the deviation resulting from the finite spatial resolution, one-dimensional interpolation was performed by positioning the set square at a tiny angle to the vertical direction. The result after the Wiener-filtering-based deblurring has shown a considerably improved profile visibility: the processed experimental contrast (0.156) increased by 30% as compared to the original one (0.12) in company with the increase in the signal-to-noise ratio (SNR) by 0.9dB. With the trend of the post-filtered experimental contrast to the theoretical one, it could be motivated that higher visibility would be achieved with the introduction of more precise blurring mask and noise spectrum estimation.

Protecting maritime borders is crucial to ensuring overall border security. Law enforcement agencies make great use of analyzing images of underwater debris to gather intelligence and detect illicit materials. Underwater image improvement contributes to better data quality and analytical. Nevertheless, underwater image analysis poses greater challenges compared to analyzing images taken above the water, factors like refraction of light and darkness contribute to the degradation of underwater image quality. In this paper, a novel approach is proposed to enhance underwater images, the proposed approach involves splitting underwater colored image to its three basic components, Subsequently, a point spread function is created for each component to describes image blurring factor, The deblurring process is then applied by using wiener filter, the result sharped by sharping filter to clarify edges, contrast linear stretch is performed to improve contrast and visual details. and the resulting image is finally reassembled from the three basic components. The proposed method showed effective results in evaluating the main metrics and gave better results when compared to a number of different methods. These results prove the effectiveness of the proposed method and its ability to practical applications in improving image quality.