Multi-frame Blind Deconvolution Research Articles

Slipping reconnection in a solar flare observed in high resolution with the GREGOR solar telescope

A small flare ribbon above a sunspot umbra in active region 12205 was observed on November 7, 2014, at 12:00 UT in the blue imaging channel of the 1.5 m GREGOR telescope, using a 1 A Ca II H interference filter. Context observations from the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO), the Solar Optical Telescope (SOT) onboard Hinode, and the Interface Region Imaging Spectrograph (IRIS) show that this ribbon is part of a larger one that extends through the neighboring positive polarities and also participates in several other flares within the active region. We reconstructed a time series of 140 seconds of Ca II H images by means of the multiframe blind deconvolution method, which resulted in spatial and temporal resolutions of 0.1 arcsec and 1 s. Light curves and horizontal velocities of small-scale bright knots in the observed flare ribbon were measured. Some knots are stationary, but three move along the ribbon with speeds of 7-11 km/s. Two of them move in the opposite direction and exhibit highly correlated intensity changes, which provides evidence of a slipping reconnection at small spatial scales.

Multi-frame partially saturated images blind deconvolution

When blurred images have saturated or over-exposed pixels, conventional blind deconvolution approaches often fail to estimate accurate point spread function (PSF) and will introduce local ringing artifacts. In this paper, we propose a method to deal with the problem under the modified multi-frame blind deconvolution framework. First, in the kernel estimation step, a light streak detection scheme using multi-frame blurred images is incorporated into the regularization constraint. Second, we deal with image regions affected by the saturated pixels separately by modeling a weighted matrix during each multi-frame deconvolution iteration process. Both synthetic and real-world examples show that more accurate PSFs can be estimated and restored images have richer details and less negative effects compared to state of art methods.

High-resolution speckle imaging through strong atmospheric turbulence.

We demonstrate that high-resolution imaging through strong atmospheric turbulence can be achieved by acquiring data with a system that captures short exposure ("speckle") images using a range of aperture sizes and then using a bootstrap multi-frame blind deconvolution restoration process that starts with the smallest aperture data. Our results suggest a potential paradigm shift in how we image through atmospheric turbulence. No longer should image acquisition and post processing be treated as two independent processes: they should be considered as intimately related.

High-resolution, high-sensitivity, ground-based solar spectropolarimetry with a new fast imaging polarimeter

Context. Remote sensing of weak and small-scale solar magnetic fields is of utmost relevance for a number of important open questions in solar physics. This requires the acquisition of spectropolarimetric data with high spatial resolution (0.1 arcsec) and low noise (1e-3 to 1e-5 of the continuum intensity). The main limitations to obtain these measurements from the ground, are the degradation of the image resolution produced by atmospheric seeing and the seeing-induced crosstalk (SIC). Aims. We introduce the prototype of the Fast Solar Polarimeter (FSP), a new ground-based, high-cadence polarimeter that tackles the above-mentioned limitations by producing data that are optimally suited for the application of post-facto image restoration, and by operating at a modulation frequency of 100 Hz to reduce SIC. Results. The pnCCD camera reaches 400 fps while keeping a high duty cycle (98.6 %) and very low noise (4.94 erms). The modulator is optimized to have high (> 80%) total polarimetric efficiency in the visible spectral range. This allows FSP to acquire 100 photon-noise-limited, full-Stokes measurements per second. We found that the seeing induced signals present in narrow-band, non-modulated, quiet-sun measurements are (a) lower than the noise (7e-5) after integrating 7.66 min, (b) lower than the noise (2.3e-4) after integrating 1.16 min and (c) slightly above the noise (4e-3) after restoring case (b) by means of a multi-object multi-frame blind deconvolution. In addition, we demonstrate that by using only narrow-band images (with low SNR of 13.9) of an active region, we can obtain one complete set of high-quality restored measurements about every 2 s.

Parallel hybrid bispectrum-multi-frame blind deconvolution image reconstruction technique

This paper presents B-MFBD, a parallel hybrid of bispectrum speckle imaging (SI) and multi-frame blind deconvolution (MFBD) image reconstruction techniques for anisoplanatic, long horizontal path imaging. Our aim is to recover an enhanced version of a turbulence-corrupted image by massive parallelization of an SI and MFBD algorithms. The bispectrum SI technique is used in place of the multi-frame ensemble averaging to initialize the iterative parallel MFBD algorithm. B-MFBD technique, through massive parallelization, provides significantly large improvement in execution speed to both the bispectrum SI and MFBD parts of the hybrid algorithm. We report \(85\,\%\) improvement in processing time with respect to the sequential implementation of the same algorithm for a \(256 \times 256\), gray-scale image, with comparable improvement in image quality.

Parallel implementation of multiframe blind deconvolution based on GPU

多帧盲解卷积图像复原技术能够进一步提高自适应光学图像的分辨力，但其算法比较复杂，处理耗时过长，对序列图像复原经常需要几分钟甚至几十分钟的计算时间，对实际应用造成了极大不便。为了提升算法的运行速度，改善其耗时过长的问题，通过研究和分析盲解卷积算法原理和算法结构，采用目前高速发展的中央处理器（CPU）和图形处理器（GPU）异构加速技术，主要对耗时最长的矩阵卷积运算进行优化，通过使用库函数与算法结构微调相结合的方法并行加速，实现多帧盲解卷积的图像复原算法的并行化。使用并行算法对图像进行复原处理，针对16帧以上分辨率为256256像素的空间目标图像，可以实现17的加速比，为图像复原的实时/准实时提供一种可行的方案。

Bilevel Optimization for Calibrating Point Spread Functions in Blind Deconvolution

Blind deconvolution problems arise in many imaging modalities, where both the underlying point spread function, which parameterizes the convolution operator, and the source image need to be identified. In this work, a novel bilevel optimization approach to blind deconvolution is proposed. The lower-level problem refers to the minimization of a total-variation model, as is typically done in non-blind image deconvolution. The upper-level objective takes into account additional statistical information depending on the particular imaging modality. Bilevel problems of such type are investigated systematically. Analytical properties of the lower-level solution mapping are established based on Robinson's strong regularity condition. Furthermore, several stationarity conditions are derived from the variational geometry induced by the lower-level problem. Numerically, a projected-gradient-type method is employed to obtain a Clarke-type stationary point and its convergence properties are analyzed. We also implement an efficient version of the proposed algorithm and test it through the experiments on point spread function calibration and multiframe blind deconvolution.

GREGOR observations of a small flare above a sunspot

AbstractA small flare ribbon above a sunspot umbra in active region 12205 was observed on November 7, 2014, at 12:00 UT in the blue imaging channel of the 1.5-m GREGOR telescope, using a 0.1 nm Ca II H interference filter. Context observations from SDO/AIA, Hinode/SOT, and IRIS show that the ribbon is a part of a larger one that extends through the neighboring positive polarities and also participates in several other flares within the active region. A 140 second long time series of Ca II H images was reconstructed by means of the Multi-Frame Blind Deconvolution method, giving the respective spatial and temporal resolutions of 0”.1 and 1 s. Light curves and horizontal velocities of small-scale bright knots in the observed flare ribbon were measured. Some knots are stationary but three move along the ribbon with speeds of 7–11 km s−1. Two of them move in the opposite direction and exhibit highly correlated intensity changes, providing evidence for the presence of slipping reconnection at small spatial scales.

Comparison of bispectrum, multiframe blind deconvolution and hybrid bispectrum-multiframe blind deconvolution image reconstruction techniques for anisoplanatic, long horizontal-path imaging

The potential benefits of real-time, or near-real-time, image processing hardware to correct for turbulence-induced image defects for long-range surveillance and weapons targeting are sufficient to motivate significant resource commitment to their development. Quantitative comparisons between potential candidates are necessary to decide on a preferred processing algorithm. We begin by comparing the mean-square-error (MSE) performance of speckle imaging (SI) methods and multiframe blind deconvolution (MFBD), applied to long-path horizontal imaging of a static scene under anisoplanatic seeing conditions. Both methods are used to reconstruct a scene from three sets of 1000 simulated images featuring low, moderate, and severe turbulence-induced aberrations. The comparison shows that SI techniques can reduce the MSE up to 47%, using 15 input frames under daytime conditions. The MFBD method provides up to 40% improvement in MSE under the same conditions. The performance comparison is repeated under three diminishing light conditions, 30, 15, 8 photons per pixel on average, where improvements of up to 39% can be achieved using SI methods with 25 input frames, and up to 38% for the MFBD method using 150 input frames. The MFBD estimator is applied to three sets of field data and representative results presented. Finally, the performance of a hybrid bispectrum-MFBD estimator that uses a rapid bispectrum estimate as the starting point for the MFBD image reconstruction algorithm is examined.

Multiframe blind image deconvolution with split Bregman method

In the paper, a multiframe blind image deconvolution method based on total variation and framelet regularizer is proposed. An adapted version of the split Bregman method is proposed to efficiently solve the resulting minimization problems. In each iteration, four sub-problems need to be solved, one of which can be very efficiently and easily solved via fast Fourier transform implementation or closed form solution. Both simulated noisy and blurred frames and real degraded frames are used to verify the effectiveness of the proposed method. Comparative experimental results show that the proposed method can efficiently remove the blur and noises and restore high quality sharp image.

Reconstruction of long horizontal-path images under anisoplanatic conditions using multiframe blind deconvolution

All optical systems that operate in or through the atmosphere suffer from turbulence induced image blur. Both military and civilian surveillance, gun sighting, and target identification systems are interested in terrestrial imaging over very long horizontal paths, but atmospheric turbulence can blur the resulting images beyond usefulness. This work explores the mean square error (MSE) performance of a multiframe blind deconvolution (MFBD) technique applied under anisoplanatic conditions for both Gaussian and Poisson noise model assumptions. The technique is evaluated for use in reconstructing images of scenes corrupted by turbulence in long horizontal-path imaging scenarios. Performance is evaluated via the reconstruction of a common object from three sets of simulated turbulence degraded imagery representing low, moderate, and severe turbulence conditions. Each set consisted of 1000 simulated turbulence degraded images. The MSE performance of the estimator is evaluated as a function of the number of images, and the number of Zernike polynomial terms used to characterize the point spread function. A Gaussian noise model-based MFBD algorithm reconstructs objects that showed as much as 40% improvement in MSE with as few as 14 frames and 30 Zernike coefficients used in the reconstruction, despite the presence of anisoplanatism in the data. An MFBD algorithm based on the Poisson noise model required a minimum of 50 frames to achieve significant improvement over the average MSE for the data set. Reconstructed objects show as much as 38% improvement in MSE using 175 frames and 30 Zernike coefficients in the reconstruction.

Research on Blind Deconvolution Algorithm of Multiframe Turbulence-degraded Images

The restoration algorithm based on frame selection and multi-frame blind deconvolution is proposed in this paper for restoring object image from a sequence of turbulence-degraded images with noise. The algorithm has been applied to Maximum-likelihood estimates as the basic principles. The logarithmic Maximum-likelihood function of multi-frame images is built according to the image Gaussian noise models, and the iterative relationships to estimate the PSFs and object image are derived by maximizing the logarithmic Maximum-likelihood function. Meanwhile, the bandwidth limit function about the support of the PSFs is estimated in view of optical system parameters and Fourier optical theory, and incorporated into the iterative process of the restoration as that increase the convergence and stability of the Maximum-likelihood algorithm. In order to test the validity of the proposed algorithm, a series of restoration experiments are performed on the sequence turbulence-degraded images with completely unknown the degradation model and the experiments results show that the proposed algorithm can effectively restore the ground-in object from their turbulence-degraded images.

Extended-object reconstruction in adaptive-optics imaging: the multiresolution approach

We propose the application of multiresolution transforms, such as wavelets (WT) and curvelets (CT), to the reconstruction of images of extended objects that have been acquired with adaptive optics (AO) systems. Such multichannel approaches normally make use of probabilistic tools in order to distinguish significant structures from noise and reconstruction residuals. Furthermore, we aim to check the historical assumption that image-reconstruction algorithms using static PSFs are not suitable for AO imaging. We convolve an image of Saturn taken with the Hubble Space Telescope (HST) with AO PSFs from the 5-m Hale telescope at the Palomar Observatory and add both shot and readout noise. Subsequently, we apply different approaches to the blurred and noisy data in order to recover the original object. The approaches include multi-frame blind deconvolution (with the algorithm IDAC), myopic deconvolution with regularization (with MISTRAL) and wavelets- or curvelets-based static PSF deconvolution (AWMLE and ACMLE algorithms). We used the mean squared error (MSE) and the structural similarity index (SSIM) to compare the results. We discuss the strengths and weaknesses of the two metrics. We found that CT produces better results than WT, as measured in terms of MSE and SSIM. Multichannel deconvolution with a static PSF produces results which are generally better than the results obtained with the myopic/blind approaches (for the images we tested) thus showing that the ability of a method to suppress the noise and to track the underlying iterative process is just as critical as the capability of the myopic/blind approaches to update the PSF.

Multi-frame blind deconvolution using sparse priors

In this paper, we propose a method for multi-frame blind deconvolution. Two sparse priors, i.e., the natural image gradient prior and an l1-norm based prior are used to regularize the latent image and point spread functions (PSFs) respectively. An alternating minimization approach is adopted to solve the resulted optimization problem. We use both gray scale blurred frames from a data set and some colored ones which are captured by a digital camera to verify the robustness of our approach. Experimental results show that the proposed method can accurately reconstruct PSFs with complex structures and the restored images are of high quality.

Reconstruction of spatially misaligned and turbulence degraded images

A reconstruction method is presented to deal with spatially misaligned and turbulence degraded images that contain shift and rotation motion. This method is implemented through bringing the channels into spatial alignment via image registration and then restoring the original images by a new multiframe blind deconvolution. A frequency domain technique is introduced to precisely register the spatially misaligned images. New priori information of temporal signatures is adopted to constrain the solution in the blind deconvolution. Results show that by the proposed method high quality image can be recovered from spatially misaligned and turbulence degraded images.

An efficient computational approach for multiframe blind deconvolution

Obtaining high resolution images of space objects from ground based telescopes involves using a combination of sophisticated hardware and computational post-processing techniques. An important, and often highly effective, computational post processing tool is multiframe blind deconvolution (MFBD). Mathematically, MFBD is modeled as a nonlinear inverse problem that can be solved using a flexible, variable projection optimization approach. In this paper we consider MFBD problems that are parameterized by a large number of variables. The formulas required for efficient implementation are carefully derived using the spectral decomposition and by exploiting properties of conjugate symmetric vectors. In addition, a new approach is proposed to provide a mathematical decoupling of the optimization problem, leading to a block structure of the Jacobian matrix. An application in astronomical imaging is considered, and numerical experiments illustrate the effectiveness of our approach.

Application of speckle and (multi-object) multi-frame blind deconvolution techniques on imaging and imaging spectropolarimetric data

We test the effects of reconstruction techniques on 2D data to determine the best approach. We obtained a time-series of spectropolarimetric data in the Fe I line at 630.25 nm with the Goettingen Fabry-Perot Interferometer (FPI) that are accompanied by imaging data at 431.3 nm and Ca II H. We apply both speckle and (MO)MFBD techniques. We compare the spatial resolution and investigate the impact of the reconstruction on spectral characteristics. The speckle reconstruction and MFBD perform similar for our imaging data with nearly identical intensity contrasts. MFBD provides a better and more homogeneous resolution at the shortest wavelength. The MOMFBD and speckle deconvolution of the intensity spectra lead to similar results, but our choice of settings for the MOMFBD yields an intensity contrast smaller by about 2% at a comparable spatial resolution. None of the reconstruction techniques introduces artifacts in the intensity spectra. The speckle deconvolution (MOMFBD) has a rms noise in V/I of 0.32% (0.20%). The deconvolved spectra thus require a high significance threshold of about 1.0%. A comparison to spectra with a significantly higher S/N ratio and to spectra from a MHD simulation reveals that the Goettingen FPI can only detect about 30% of the polarization signal in quiet Sun. The distribution of NCP values for the speckle-deconvolved data matches that of observations with higher S/N better than MOMFBD, but shows seemingly artificially sharp boundaries and unexpected changes of the sign. For the spectropolarimetric data, the higher intensity contrast of the speckle deconvolution is balanced by the smaller amplification of the noise level in the MOMFBD at a comparable spatial resolution. The noise level prevents the detection of weak and diffuse magnetic fields.

Deconvolution of astronomical images using SOR with adaptive relaxation

We address the potential performance of the successive overrelaxation technique (SOR) in image deconvolution, focusing our attention on the restoration of astronomical images distorted by atmospheric turbulence. SOR is the classical Gauss-Seidel iteration, supplemented with relaxation. As indicated by earlier work, the convergence properties of SOR, and its ultimate performance in the deconvolution of blurred and noisy images, can be made competitive to other iterative techniques, including conjugate gradients, by a proper choice of the relaxation parameter. The question of how to choose the relaxation parameter, however, remained open, and in the practical work one had to rely on experimentation. In this paper, using constructive (rather than exact) arguments, we suggest a simple strategy for choosing the relaxation parameter and for updating its value in consecutive iterations to optimize the performance of the SOR algorithm (and its positivity-constrained version, +SOR) at finite iteration counts. We suggest an extension of the algorithm to the notoriously difficult problem of "blind" deconvolution, where both the true object and the point-spread function have to be recovered from the blurred image. We report the results of numerical inversions with artificial and real data, where the algorithm is compared with techniques based on conjugate gradients. In all of our experiments +SOR provides the highest quality results. In addition +SOR is found to be able to detect moderately small changes in the true object between separate data frames: an important quality for multi-frame blind deconvolution where stationarity of the object is a necesessity.

Online multi-frame blind deconvolution with super-resolution and saturation correction

Astronomical images taken by ground-based telescopes suffer degradation due to atmospheric turbulence. This degradation can be tackled by costly hardware-based approaches such as adaptive optics, or by sophisticated software-based methods such as lucky imaging, speckle imaging, or multi-frame deconvolution. Software-based methods process a sequence of images to reconstruct a deblurred high-quality image. However, existing approaches are limited in one or several aspects: (i) they process all images in batch mode, which for thousands of images is prohibitive; (ii) they do not reconstruct a super-resolved image, even though an image sequence often contains enough information; (iii) they are unable to deal with saturated pixels; and (iv) they are usually non-blind, i.e., they assume the blur kernels to be known. In this paper we present a new method for multi-frame deconvolution called online blind deconvolution (OBD) that overcomes all these limitations simultaneously. Encouraging results on simulated and real astronomical images demonstrate that OBD yields deblurred images of comparable and often better quality than existing approaches.

CONVECTIVE NATURE OF SUNSPOT PENUMBRAL FILAMENTS: DISCOVERY OF DOWNFLOWS IN THE DEEP PHOTOSPHERE

We study the velocity structure of penumbral filaments in the deep photosphere to obtain direct evidence for the convective nature of sunspot penumbrae. A sunspot was observed at high spatial resolution with the 1-m Swedish Solar Telescope in the deep photospheric C I 5380 {\AA} absorption line. The Multi-Object Multi-Frame Blind Deconvolution (MOMFBD) method is used for image restoration and straylight is filtered out. We report here the discovery of clear redshifts in the C I 5380 {\AA} line at multiple locations in sunspot penumbral filaments. For example, bright head of filaments show larger concentrated blueshift and are surrounded by darker, redshifted regions, suggestive of overturning convection. Elongated downflow lanes are also located beside bright penumbral fibrils. Our results provide the strongest evidence yet for the presence of overturning convection in penumbral filaments and highlight the need to observe the deepest layers of the penumbra in order to uncover the energy transport processes taking place there.