Multi-frame Blind Deconvolution Algorithm Research Articles

Solar multiobject multiframe blind deconvolution with a spatially variant convolution neural emulator

The study of astronomical phenomena through ground-based observations is always challenged by the distorting effects of Earth's atmosphere. Traditional methods of post facto image correction, essential for correcting these distortions, often rely on simplifying assumptions that limit their effectiveness, particularly in the presence of spatially variant atmospheric turbulence. Such cases are often solved by partitioning the field of view into small patches, deconvolving each patch independently, and merging all patches together. This approach is often inefficient and can produce artifacts. Recent advancements in computational techniques and the advent of deep learning offer new pathways to address these limitations. This paper introduces a novel framework leveraging a deep neural network to emulate spatially variant convolutions, offering a breakthrough in the efficiency and accuracy of astronomical image deconvolution. By training on a dataset of images convolved with spatially invariant point spread functions and validating its generalizability to spatially variant conditions, this approach presents a significant advancement over traditional methods. The convolution emulator is used as a forward model in a multiobject multiframe blind deconvolution algorithm for solar images. The emulator enables the deconvolution of solar observations across large fields of view without resorting to patch-wise mosaicking, thus avoiding the artifacts associated with such techniques. This method represents a significant computational advantage, reducing processing times by orders of magnitude.

Improving closely spaced dim object detection through multiframe blind deconvolution of near stellar neighbourhoods

A new iterative algorithm is proposed to improve the detection of dim stellar objects that are in the neighbourhood of a bright object, using short-exposure images. This method separates data functions into the primary bright object function, the neighbourhood system function, and the background function. This approach uses the principles of the Expectation-Maximization algorithm with the Gerchberg-Saxton phase retrieval algorithm to overcome the image degradation caused by the photon counting noise from the charge-coupled devices and the turbulent atmospheric conditions. The performance of this new neighbourhood system algorithm is compared with that of the multiframe blind deconvolution algorithm, using laboratory data and computer-simulated data. This paper provides an improved technique to image closely spaced dim objects.

High-Fidelity Imaging Using Compact Multi-Frame Blind Deconvolution

Multi-frame blind deconvolution (MFBD) has been a cornerstone for ground-based space situational awareness of near-Earth satellites since the early 2000’s. In 2011 a variation of the classic MFBD algorithm was introduced that required solving for fewer variables than in the classic algorithm, but which still used all the available data to constrain the solution. The initial application of the new approach, referred to as compact multi-frame blind deconvolution (CMFBD), was found to be significantly faster than MFBD, and showed an indication that it may be able to provide restorations of higher quality, i.e. fewer artifacts. Since its introduction, the CMFBD approach has become the foundation of several MFBD-based algorithms that have been developed for applications such as high-accuracy wave front sensing from image plane data, and imaging through strong turbulence: both of which contribute to space situational awareness by increasing the area of sky available for surveillance. Here we show that the performance of the CMFBD approach can be improved through the addition of a new ”internal consistency” constraint on the estimated point-spread functions.

Parallel blind deconvolution reconstruction using phase diversity for three-dimensional imaging

Phase diversity (PD) is the special case of multiframe blind deconvolution algorithm, where an object is observed with a stack intentionally defocused image. The known defocused information between phases is used to estimate the object and aberration function. This paper presents a parallel three-dimensional (3-D) wavefront aberration estimation using blind deconvolution. We used multiplane PD for widefield fluorescence microscopy images of 3-D objects. Parallel multiplane PD yields a speedup factor of 4.33 with respect to the counterpart sequential algorithm for an image size of 256×256. We used the outcome of the PD algorithm to generate 3-D point cloud data from images from one view.

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.

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.

Compact multiframe blind deconvolution

We describe a multiframe blind deconvolution (MFBD) algorithm that uses spectral ratios (the ratio of the Fourier spectra of two data frames) to model the inherent temporal signatures encoded by the observed images. In addition, by focusing on the separation of the object spectrum and system transfer functions only at spatial frequencies where the measured signal is above the noise level, we significantly reduce the number of unknowns to be determined. This "compact" MFBD yields high-quality restorations in a much shorter time than is achieved with MFBD algorithms that do not model the temporal signatures; it may also provide higher-fidelity solutions.

Fast and optimal multiframe blind deconvolution algorithm for high-resolution ground-based imaging of space objects

We report a multiframe blind deconvolution algorithm that we have developed for imaging through the atmosphere. The algorithm has been parallelized to a significant degree for execution on high-performance computers, with an emphasis on distributed-memory systems so that it can be hosted on commodity clusters. As a result, image restorations can be obtained in seconds to minutes. We have compared and quantified the quality of its image restorations relative to the associated Cramér-Rao lower bounds (when they can be calculated). We describe the algorithm and its parallelization in detail, demonstrate the scalability of its parallelization across distributed-memory computer nodes, discuss the results of comparing sample variances of its output to the associated Cramér-Rao lower bounds, and present image restorations obtained by using data collected with ground-based telescopes.

Approaches to convergence enhancement of multiframe blind deconvolution of astronomical images

The convergence property of multiframe blind deconvolution applied to improve the resolution of astronomical images is influenced by initial object estimate, constraint conditions and photon noise. A method of determining band limitation of the point-spread function from the optical imaging system parameters is proposed. The initial object estimate of blind deconvolution algorithm is formed by using Knox-Thompson method to reconstruct object phase from short-exposure images. A novel method，which is used to decrease photon noise, edge effects and ringing of restoration images is developed. An improved multiframe blind deconvolution algorithm with a strict constrained optimization method of the expectation-maximization based on maximum-likelihood estimation is presented. The restored images of the simulation data,and real Mars and solar-granulation data show that the proposed multiframe blind deconvolution algorithm is valid for overcoming effects of atmospheric turbulence and photon noise and improving the resolution of real observed astronomical images, and capabel of partly removing th diffraction effect of optical system on restored images.

Restoration of turbulence degraded images using improved multi-frame blind deconvolution algorithm

For the restoration of astronomical objects which are degraded mainly by turbulence, an improved multi-frame blind deconvolution method using the generalised expectation-maximisation (GEM) on the basis of the penalised maximum-likelihood estimation method is presented. Experimental results indicate that this method has better performance and costs less time.

Iterative multiframe superresolution algorithms for atmospheric-turbulence-degraded imagery

The subject of interest is the superresolution of atmospheric-turbulence-degraded, short-exposure imagery, where superresolution refers to the removal of blur caused by a diffraction-limited optical system along with recovery of some object spatial-frequency components outside the optical passband. Photon-limited space object images are of particular interest. Two strategies based on multiple exposures are explored. The first is known as deconvolution from wave-front sensing, where estimates of the optical transfer function (OTF) associated with each exposure are derived from wave-front-sensor data. New multiframe superresolution algorithms are presented that are based on Bayesian maximum a posteriori and maximum-likelihood formulations. The second strategy is known as blind deconvolution, in which the OTF associated with each frame is unknown and must be estimated. A new multiframe blind deconvolution algorithm is presented that is based on a Bayesian maximum-likelihood formulation with strict constraints incorporated by using nonlinear reparameterizations. Quantitative simulation of imaging through atmospheric turbulence and wave-front sensing are used to demonstrate the superresolution performance of the algorithms.