Atmospheric Point Spread Function Research Articles

Data reduction and restoration of spectropolarimetric microlensed hyperspectral imager data

Context. A microlensed hyperspectral imager (MiHI) is an integral field spectrograph based on a double-sided microlens array.Aims. To convert the raw data frames of such an instrument to hyperspectral cubes, and restore them to high-resolution science-ready Stokes data, a new kind of data reduction procedure is required.Methods. An optimized ad hoc transfer map of a MiHI prototype was used to convert the raw data into raw hyperspectral form. The transfer map was modified to match the position of the image on the detector, which was found to drift considerably during the course of an observing day. The determination of this position was used to move the recorded flat-field images to the position of the observation, which was found to be a critical step in the accurate gain correction of the raw data cubes. The converted data were found to be suitable for image restoration but still contained unwanted polarimetric structure that needed to be removed.Results. The extracted and restored data were found to be of a similar spatial resolution as the equivalent data from the context imager, while retaining a spectral resolution of approximately 300 000. The noise properties of the raw data were determined by the photon statistics and were found to be consistent with the estimated transparency of the instrument and the integration time of the image sensor. As for all image-restored data, the noise properties of the restored data were found to be dependent on the instrumental and atmospheric point spread function. An attempt to compare the data to other similar data suggested that the data had a spectral and spatial information content comparable to that of a Hinode spectro-polarimetric scan, but with a higher spatial resolution and a temporal cadence of approximately 10s.

Solar Speckle Image Deblurring With Deep Prior Constraint Based on Regularization

The solar speckle image has the characteristics with single features, more noise, and blurred local details. Most of the existing deep learning deblurring methods for solar speckle images have some problems, such as high-frequency loss, artifact generation, and dependence on the paired image. In this paper, a deep prior deblurring method fusing the regularization model and prior constraint network is proposed. Firstly, the traditional handcrafted regularization priors are added to the network parameterized blind deconvolution model. The image gradient prior and blur kernel initial parameters are respectively used to the network parameterization process of two variables in the blind deconvolution model, which are the latent clean image variables and blur kernel variables. After that, the solar speckle image deep prior deblurring model is established. Secondly, the blur kernel generation network input is estimated by using the atmospheric point spread function (PSF) to improve the model convergence speed. Thirdly, a latent clean image generation network including joint gradient branching and Feature Pyramid Network (FPN) structure is designed to enhance image local edge details. Finally, a joint loss function including pixel loss, image prior loss, and mean squared error (MSE) loss is introduced to guide the model for alternate training. It can obtain the best parameter values of latent clean image and blur kernel, and achieve the solar speckle image high-resolution reconstruction. The experimental results show that the proposed method can eliminate the dependence on the reference image, and the reconstructed image has less noise and more obvious high-frequency details, faster network convergence, and two evaluation indicators of Peak Signal Noise Ratio (PSNR) and Structural Similarity (SSIM) are significantly improved.

Multi-frame image restoration method for Luojia 1-01 night-light remote sensing images

ABSTRACT To address blur and noise issues caused by airlight and atmospheric scattering in nighttime imaging environment, we proposed a multi-frame image restoration method. First, the Luojia 1–01 night-light image degradation model was derived. Thereafter, the APSF (Atmospheric Point Spread Function) for night-light images was estimated. Improved dark channel prior and sparse constraint models were used to eliminate effects ofairlight and atmospheric scattering. Finally, a multi-frame sparse constraint model was established to eliminate image noise. The results show the proposed method is effective as it can reduce the image blur phenomenon, suppress image noise, and improve image quality.

An In-Flight Radiometric Calibration Method Considering Adjacency Effects for High-Resolution Optical Sensors Over Artificial Targets

When using a field calibration site to perform in-orbit radiometric calibration of a space-borne remote sensor, the measured signal by the sensor may contain radiation from adjacent pixels, due to scattering in the Earth’s atmosphere and the sensor viewing characteristics. If this is not accounted for in the modeling, the accuracy of the radiometric calibration will be reduced. Nonuniformities of the ground target are more significant for the calibration of high-resolution sensors. In addition, if the brightness contrast of neighboring targets is not small, the impact of the adjacency effects will be more significant. It is important to quantitatively analyze and estimate the influence of this kind of adjacency effects, to reduce the uncertainty of in-orbit radiometric calibration. To evaluate the adjacency effects caused by atmospheric multiple scattering, this article constructed a local atmospheric point spread function model using long time-series satellite-ground synchronous observation data and developed an adjacency effects simulation method, which considers background reflectance spectral information. Tests on Sentinel-2A and Worldview-3 imagery overpassing the Baotou calibration and validation site (Baotou C&V site) (China) indicate that the proposed modeling method can effectively account for the influence of the adjacency effects in vicarious calibration. Uncertainties of relevant parameters and their contributions to the calibration result were also analyzed, and uncertainty assessment results show that the vicarious radiometric calibration scheme considering adjacency effects correction can bring about a total uncertainty less than 7%.

Single Image Dehazing of Road Scenes Using Spatially Adaptive Atmospheric Point Spread Function

Image haze removal is essential in autonomous driving as the outdoor images captured during unfavorable weather conditions, such as haze or snow, are affected by poor visibility. Much research has been done to overcome image degradation such as low contrast and faded color due to haze. However, in the traditional model, a phenomenon is neglected that several particles simultaneously involved in light acquisition. To address this problem, we propose a novel single image dehazing method based on the spatially adaptive atmospheric point spread function (APSF). We developed a module that estimates the APSF to overcome the limitations of the spatially invariant APSF which used in existing dehazing algorithms. The key factor in the estimation is that road scenes with haze have different statistical characteristic from common hazy images in color and resolution. Furthermore, the APSF on the traffic signs or lights is estimated by generating superpixels to prevent halo artifacts around the sharp edges of the images. We adopted the total variation model as a regularization functional to reduce halo and unnatural artifacts that may occur during deconvolution. The haze-free images from the proposed method tested whether the proposed method can enhance the performance of vision algorithms for autonomous driving. The experimental results demonstrate that the proposed method outperforms state-of-the-art image dehazing methods enhancing the performance of the vision algorithms. Moreover, additional experiments demonstrated the effectiveness of the proposed method for quantitative and qualitative comparison with the state-of-the-art algorithms.

CWV-Net: A Deep Neural Network for Atmospheric Column Water Vapor Retrieval From Hyperspectral VNIR Data

Estimation of the total column water vapor (CWV) content of the atmosphere plays an important role in the atmospheric compensation (AC) of remotely sensed hyperspectral images collected in the visible and near infrared (VNIR) spectral range. Most of the proposed CWV retrieval methods provide accurate estimates as long as other significant atmospheric parameters are known. Those parameters are not generally available and must in turn be estimated. In this article, a new approach based on deep learning is proposed that allows the estimation of CWV without the knowledge of the atmospheric visibility, the solar zenith angle, and the atmospheric point spread function (PSF). The proposed approach includes a training strategy based on synthetic data that are generated according to an accurate radiative-transfer model, and by exploiting reflectance spectral libraries and the MODTRAN radiative-transfer code. Experiments on simulated data are carried out to analyze the performance of the proposed deep neural network with reference to both aerial and satellite applications. Furthermore, an example of the results provided by the network in a real application is shown. For this purpose, the network is applied to data acquired by an airborne hyperspectral sensor operating in the VNIR spectral range.

Night-light Image Restoration Method Based on Night Scattering Model for Luojia 1-01 Satellite.

Aiming at solving the degradation problem of Luojia 1-01 night-light remote sensing images, the main reason for the “glow” phenomenon was analyzed. The APSF (Atmospheric Point Spread Function) template of night-light image was obtained from atmospheric source scattering. The template was used as the initial value in the regularization restoration model in this paper. Experiments were carried out using single point and regional images. The results demonstrate that the estimated APSF and restoration results of the method are better than those from other methods, and the image quality is improved after restoration.

Synthesis of atmospheric turbulence point spread functions by sparse and redundant representations

Atmospheric turbulence is a fundamental problem in imaging through long slant ranges, horizontal-range paths, or uplooking astronomical cases through the atmosphere. An essential characterization of atmospheric turbulence is the point spread function (PSF). Turbulence images can be simulated to study basic questions, such as image quality and image restoration, by synthesizing PSFs of desired properties. In this paper, we report on a method to synthesize PSFs of atmospheric turbulence. The method uses recent developments in sparse and redundant representations. From a training set of measured atmospheric PSFs, we construct a dictionary of “basis functions” that characterize the atmospheric turbulence PSFs. A PSF can be synthesized from this dictionary by a properly weighted combination of dictionary elements. We disclose an algorithm to synthesize PSFs from the dictionary. The algorithm can synthesize PSFs in three orders of magnitude less computing time than conventional wave optics propagation methods. The resulting PSFs are also shown to be statistically representative of the turbulence conditions that were used to construct the dictionary.

Visibility Restoration for Single Hazy Image Using Dual Prior Knowledge

Single image haze removal has been a challenging task due to its super ill-posed nature. In this paper, we propose a novel single image algorithm that improves the detail and color of such degraded images. More concretely, we redefine a more reliable atmospheric scattering model (ASM) based on our previous work and the atmospheric point spread function (APSF). Further, by taking the haze density spatial feature into consideration, we design a scene-wise APSF kernel prediction mechanism to eliminate the multiple-scattering effect. With the redefined ASM and designed APSF, combined with the existing prior knowledge, the complex dehazing problem can be subtly converted into one-dimensional searching problem, which allows us to directly obtain the scene transmission and thereby recover visually realistic results via the proposed ASM. Experimental results verify that our algorithm outperforms several state-of-the-art dehazing techniques in terms of robustness, effectiveness, and efficiency.

人工神经网络计算大气点扩展函数

人工神经网络计算大气点扩展函数

Atmospheric correction of high-spatial-resolution satellite images with adjacency effects: application to EO-1 ALI data

Adjacency effects are an interesting physical phenomenon caused by multiple scattering between the atmosphere and the surface. It is necessary to remove adjacency effects in the surface reflectance retrieved from satellite data at a high spatial resolution. In this study, we propose an atmospheric correction method with adjacency effect correction to derive surface reflectance from Earth Observing-1 (EO-1) Advanced Land Imager (ALI) data. Adjacency effects are corrected using an atmospheric point spread function. An analytical expression of the atmospheric point spread function is presented based on a single scattering approximation. This method was applied to ALI imagery acquired through Watershed Airborne Telemetry Experimental Research (WATER) on 20 May 2008. Compared with the surface reflectance before the adjacency effects were corrected for, the surface reflectance after correction exhibited increased between-pixel contrast. Furthermore, the discrepancies between the surface reflectance before and after corrections decreased from the blue band to the shortwave infrared band.

Toward Understanding the Anisotropic Point Spread Function of Suprime-Cam and Its Impact on Cosmic Shear Measurement

We examined the anisotropic point spread function (PSF) of Suprime-Cam data utilizing dense star field data. We decomposed the PSF ellipticities into three components—the optical aberration, atmospheric turbulence, and chip misalignment in an empirical manner—and evaluated the amplitude of each component. We then tested a standard method for correcting the PSF ellipticities used in weak lensing analysis against a mock simulation. We found that, for long-exposure data, the optical aberration has the largest contribution to the PSF ellipticities, which could be modeled well by a simple analytic function based on the lowest-order aberration theory. The statistical properties of PSF ellipticities resulting from atmospheric turbulence were investigated by using numerical simulations. The simulation results are in a reasonable agreement with the observed data. It follows from these findings that the spatial variation of PSF ellipticities consists of two components: one is a smooth and parametrizable component arising from the optical PSF, and the other is a non-smooth and stochastic component resulting from the atmospheric PSF. The former can be well corrected by the standard correction method with a polynomial fitting function. However, for the latter, its correction is affected by the common limitation caused by sparse sampling of PSFs due to a limited number of stars. We also examined the effects of the residual PSF anisotropies on Suprime-Cam cosmic shear data (5.6-degree2 of i′-band data). We found that the shape and amplitude of the B-mode shear variance are broadly consistent with those of the residual PSF ellipticities measured from the dense star field data. This indicates that most of the sources of residual systematic are understood, which is an important step for cosmic shear statistics to be a practical tool of the precision cosmology.

Monte Carlo simulation of light scattering in the atmosphere and effect of atmospheric aerosols on the point spread function

We present a Monte Carlo simulation for the scattering of light in the case of an isotropic light source. The scattering phase functions are studied particularly in detail to understand how they can affect the multiple light scattering in the atmosphere. We show that, although aerosols are usually in lower density than molecules in the atmosphere, they can have a non-negligible effect on the atmospheric point spread function. This effect is especially expected for ground-based detectors when large aerosols are present in the atmosphere.

Atmospheric point spread function interpolation for weak lensing in short exposure imaging data

A main science goal for the Large Synoptic Survey Telescope (LSST) is to measure the cosmic shear signal from weak lensing to extreme accuracy. One difficulty, however, is that with the short exposure time ($\simeq$15 seconds) proposed, the spatial variation of the Point Spread Function (PSF) shapes may be dominated by the atmosphere, in addition to optics errors. While optics errors mainly cause the PSF to vary on angular scales similar or larger than a single CCD sensor, the atmosphere generates stochastic structures on a wide range of angular scales. It thus becomes a challenge to infer the multi-scale, complex atmospheric PSF patterns by interpolating the sparsely sampled stars in the field. In this paper we present a new method, PSFent, for interpolating the PSF shape parameters, based on reconstructing underlying shape parameter maps with a multi-scale maximum entropy algorithm. We demonstrate, using images from the LSST Photon Simulator, the performance of our approach relative to a 5th-order polynomial fit (representing the current standard) and a simple boxcar smoothing technique. Quantitatively, PSFent predicts more accurate PSF models in all scenarios and the residual PSF errors are spatially less correlated. This improvement in PSF interpolation leads to a factor of 3.5 lower systematic errors in the shear power spectrum on scales smaller than $\sim13'$, compared to polynomial fitting. We estimate that with PSFent and for stellar densities greater than $\simeq1/{\rm arcmin}^{2}$, the spurious shear correlation from PSF interpolation, after combining a complete 10-year dataset from LSST, is lower than the corresponding statistical uncertainties on the cosmic shear power spectrum, even under a conservative scenario.

Spurious shear in weak lensing with the Large Synoptic Survey Telescope

The complete 10-year survey from the Large Synoptic Survey Telescope (LSST) will image $\sim$ 20,000 square degrees of sky in six filter bands every few nights, bringing the final survey depth to $r\sim27.5$, with over 4 billion well measured galaxies. To take full advantage of this unprecedented statistical power, the systematic errors associated with weak lensing measurements need to be controlled to a level similar to the statistical errors. This work is the first attempt to quantitatively estimate the absolute level and statistical properties of the systematic errors on weak lensing shear measurements due to the most important physical effects in the LSST system via high fidelity ray-tracing simulations. We identify and isolate the different sources of algorithm-independent, \textit{additive} systematic errors on shear measurements for LSST and predict their impact on the final cosmic shear measurements using conventional weak lensing analysis techniques. We find that the main source of the errors comes from an inability to adequately characterise the atmospheric point spread function (PSF) due to its high frequency spatial variation on angular scales smaller than $\sim10'$ in the single short exposures, which propagates into a spurious shear correlation function at the $10^{-4}$--$10^{-3}$ level on these scales. With the large multi-epoch dataset that will be acquired by LSST, the stochastic errors average out, bringing the final spurious shear correlation function to a level very close to the statistical errors. Our results imply that the cosmological constraints from LSST will not be severely limited by these algorithm-independent, additive systematic effects.

Simulation model based approach for long exposure atmospheric point spread function reconstruction for laser guide star multiconjugate adaptive optics

This paper discusses an innovative simulation model based approach for long exposure atmospheric point spread function (PSF) reconstruction in the context of laser guide star (LGS) multiconjugate adaptive optics (MCAO). The approach is inspired from the classical scheme developed by Véran et al. [J. Opt. Soc. Am. A14, 3057 (1997)] and Flicker et al. [Astron. Astrophys.400, 1199 (2003)] and reconstructs the long exposure optical transfer function (OTF), i.e., the Fourier transformed PSF, as a product of separate long-exposure tip/tilt removed and tip/tilt OTFs, each estimated by postprocessing system and simulation telemetry data. Sample enclosed energy results assessing reconstruction accuracy are presented for the Thirty Meter Telescope LGS MCAO system currently under design and show that percent level absolute and differential photometry over a 30 arcsec diameter field of view are achievable provided the simulation model faithfully represents the real system.

A fast empirical method for galaxy shape measurements in weak lensing surveys

We describe a simple and fast method to correct ellipticity measurements of galaxies from the distortion by the instrumental and atmospheric point spread function (PSF), in view of weak lensing shear measurements. The method performs a classification of galaxies and associated PSFs according to measured shape parameters, and corrects the measured galaxy ellipticites by querying a large lookup table (LUT), built by supervised learning. We have applied this new method to the GREAT10 image analysis challenge, and present in this paper a refined solution that obtains the competitive quality factor of Q = 104, without any shear power spectrum denoising or training. Of particular interest is the efficiency of the method, with a processing time below 3 ms per galaxy on an ordinary CPU.

Estimation of Normalized Atmospheric Point Spread Function and Restoration of Remotely Sensed Images

The Earth's atmosphere heavily affects the remote sensing images collected by spaceborne passive optical sensors due to radiation–matter interaction phenomena like radiation absorption, scattering, and thermal emission. A complex phenomenon is the adjacency effect, i.e., radiation reflected by the ground that, due to the atmospheric scattering, is being seen in a viewing direction different from that corresponding to the ground location that reflected it. Adjacency gives rise to crosstalk between neighboring picture elements up to a distance that depends on the width of the integral kernel function employed for the mathematical modeling of the problem. As long as the atmosphere is a linear space-invariant system, the adjacency can be modeled as a low-pass filter, with the atmospheric point spread function (APSF) applied to the initial image. In this paper, a direct method of estimating the discrete normalized APSF (NAPSF) using images gathered by high-resolution optical sensors is discussed. We discuss the use of the NAPSF estimate for deducing the Correction Spatial high-pass Filter (CSF)—a correction filter that removes the adjacency effect. The NAPSF estimation procedure has been investigated using statistical simulations, whose outcomes permitted us to identify the conditions under which the NAPSF could be measured with acceptable errors. The NAPSF estimation is examined for various natural images acquired by MOMS-2P, CHRIS, AVIRIS, and MIVIS.

Velocities Measured in Small‐Scale Solar Magnetic Elements

We have obtained high resolution spectrograms of small scale magnetic structures with the Swedish 1-m Solar Telescope. We present Doppler measurements at $0\farcs{2}$ spatial resolution of bright points, ribbons and flowers and their immediate surroundings, in the C {\small{I}} 5380.3 {\AA} line (formed in the deep photosphere) and the two Fe {\small{I}} lines at 5379.6 {\AA} and 5386.3 {\AA}. The velocity inside the flowers and ribbons are measured to be almost zero, while we observe downflows at the edges. These downflows are increasing with decreasing height. We also analyze realistic magneto-convective simulations to obtain a better understanding of the interpretation of the observed signal. We calculate how the Doppler signal depends on the velocity field in various structures. Both the smearing effect of the non-negligible width of this velocity response function along the line of sight and of the smearing from the telescope and atmospheric point spread function are discussed. These studies lead us to the conclusion that the velocity inside the magnetic elements are really upflow of the order 1--2 km s${}^{-1}$ while the downflows at the edges really are much stronger than observed, of the order 1.5--3.3 km s${}^{-1}$.

On encoding neural networks to estimate the atmospheric point spread function in a parallel environment

Images of the Earth's surface acquired by high-altitude aircraft or satellites are degraded by the intervening atmosphere. The imaging instrument records not only the signal of the targeted viewing area but also the radiance scattered into the field of view in the near by area. This effect can be characterized by an atmospheric point spread function (PSF). There are many parameters that may affect the PSF. To restore noisy-blurred images, one must understand which parameters influence the PSF and to what degree. This is very important for scientific applications that seek to extract information about environmental systems. In this paper, a design and implementation of a distributed representation scheme and neural networks are presented in order to estimate the atmospheric PSF. The representation scheme exemplifies the conjunctive coding and coarse coding techniques. Neural networks trained using such an appropriately structured representation generate a desired approximation of the PSF with satisfactory processing time. The system is implemented on a parallel system formed of personal computers and workstations. The maximum speed-up is achieved.