Non-parametric PSF estimation from celestial transit solar images using blind deconvolution

Abstract

Context: Characterization of instrumental effects in astronomical imaging is important in order to extract accurate physical information from the observations. The measured image in a real optical instrument is usually represented by the convolution of an ideal image with a Point Spread Function (PSF). Additionally, the image acquisition process is also contaminated by other sources of noise (read-out, photon-counting). The problem of estimating both the PSF and a denoised image is called blind deconvolution and is ill-posed. Aims: We propose a blind deconvolution scheme that relies on image regularization. Contrarily to most methods presented in the literature, our method does not assume a parametric model of the PSF and can thus be applied to any telescope. Methods: Our scheme uses a wavelet analysis prior model on the image and weak assumptions on the PSF. We use observations from a celestial transit, where the occulting body can be assumed to be a black disk. These constraints allow us to retain meaningful solutions for the filter and the image, eliminating trivial, translated, and interchanged solutions. Under an additive Gaussian noise assumption, they also enforce noise canceling and avoid reconstruction artifacts by promoting the whiteness of the residual between the blurred observations and the cleaned data. Results: Our method is applied to synthetic and experimental data. The PSF is estimated for the SECCHI/EUVI instrument using the 2007 Lunar transit, and for SDO/AIA using the 2012 Venus transit. Results show that the proposed non-parametric blind deconvolution method is able to estimate the core of the PSF with a similar quality to parametric methods proposed in the literature. We also show that, if these parametric estimations are incorporated in the acquisition model, the resulting PSF outperforms both the parametric and non-parametric methods.

x-Raying the Dark Side of Venus—scatter From Venus’ Magnetotail?

“…They, too, assumed that no EUV radiation comes from Venus' dark side, and then found that "much more scattered light is found than can be accounted for merely by diffraction" and that half of the scattered light was due to some other mechanism. It is quite possible that both González et al (2016) and DeForest et al (2009) had discovered, and were trying to account for, some real EUV emission of the kind we find. For the above reasons we decided to adopt the PSF derived in Grigis et al (2012), which is available in SSW and is a standard in deconvolving AIA EUV images.…”

“…Poduval et al (2013) derived the in-flight SDO/AIA PSF using several observations, including some of the Moon's limb made during a solar eclipse observed with SDO/AIA. González et al (2016) used observations of both a solar eclipse and a Venus transit to derive the SDO/AIA PSF. These authors assumed that there is no emission coming from the dark side of Venus during the transit, but then discovered that they needed to include a long range effect, otherwise the parametric PSF they used would not be able to remove "the apparent emission inside the disk of Venus".…”

Simultaneous High Dynamic Range Algorithm, Testing, and Instrument Simulation

“…We also convolved the point spread func- tion from the Zemax model with the input image. Note that for flight, the smearing effects of the point spread function will be mitigated using well-established EUV imager deconvolution methods (e.g., Schwartz et al 2015;González et al 2016;Seaton et al 2013a) but those enhancements are not applied in these simulations, meaning our results are conservative in this respect.…”

Multispectral Compressive Imaging Strategies Using Fabry–Pérot Filtered Sensors

“…Keep in mind that the modeled diffraction kernel is an approximation based on assumptions such as the use of a perfect thin lens, the fact that the object is an incoherent plane wave, etc. The actual PSF of the system could be measured, for instance, using a pre-defined CA along with a point-like target light source to estimate the PSF with a regularized inverse problem (see, e.g., [65,66]). Spatially-dependent PSFs could also be estimated with similar techniques.…”