High-resolution anisoplanatic imaging from an airborne platform

The main inherent property of medical ultrasound imaging is speckle noise which generally obscures and reduces the diagnostic image resolution and contrast. Consequently, the substantial improvement of ultrasound images is an important prerequisite, whenever ultrasound is used as one of the most utilized diagnostic modalities. The problem of reconstruction of ultrasound images by means of blind deconvolution has long been recognized as a prerequisite in ultrasound imaging processing in the recent decades. Recently, the Total Variation (TV) regularization method has become extremely effective approach for image reconstruction, especially for restoring edges of the blurring image. In this paper, we present a new blind iterative TV deconvolution algorithm for reconstructing ultrasound images from blurry and noisy observations. First, it proposes the initial estimation of the point-spread function (PSF) based on a generalization of two-dimensional homomorphic filtering in cepstrum domain. It is demonstrated that the initial PSF can be effectively estimated by applying a proper smoothing low-pass filtering in cepstrum domain. Second, it introduces a novel blind iterative TV deconvolution which is derived from an alternating minimization algorithm. Fast Fourier Transform (FFT) is used in the pre-iteration computation. The innovative blind deconvolution is based on either concurrent or successive estimation of the PSF function and the image of interest. The iterative scheme is devised to recover the image and simultaneously identify the PSF function. The estimated PSF and restored image will be close to real values in the subsequent iterative deconvolution. Final, tests on phantom and clinical images have proven our novel blind iterative TV deconvolution gives more positive results of higher spatial resolution and better defined tissue structures than other deconvolution methods.

This paper reviews the energy efficiency of several popular block-matching motion estimation algorithms that can be used in wireless video sensor network applications. Full search motion estimation provides the best image quality but requires high computing power. Therefore, only fast search algorithms are considered for deployment in wireless video sensor networks, which generally operate in remote, battery-constrained areas. However, the image quality of fast search algorithms also needs to be considered in the comparison. In this paper, those block-matching algorithms are compared using two criteria: 1) computing cost/energy consumption, and 2) image quality. The purpose of this review is to select the most energy efficient algorithm that retains image quality and can be applied to wireless video sensor network video compression applications.

Studying binary systems with faint close companions requires high-resolution and high-contrast imaging. The ‘Alopeke speckle instrument, located at Gemini North, detects such binary systems. We optimize the spatial resolution in our speckle data using the multi-frame blind deconvolution (MFBD) technique. However, as the instrumental optical aberrations are static, their contribution to the blurring of the image remains in the digitally restored object estimate, in effect limiting the achievable resolution. This residual image blur can fortunately be removed by an additional single-frame blind deconvolution. The estimated aberrations from this second step show excellent agreement with the expectations for the aberrations of the ‘Alopeke instrument and pave the way to improving the optical alignment. Furthermore this technique can be used for any speckle imaging system.

The present paper illustrates a geodesic-based learning algorithm over a curved parameter space for blind deconvolution application. The chosen deconvolving structure appears as a single neuron model whose learning rule arises from criterion-function minimization over a smooth manifold. In particular, we propose here a learning stepsize selection theory for the algorithm at hand. We consider the blind deconvolution performances of the algorithm as well as its computational burden. Also, a numerical comparison with seven blind-deconvolution algorithms known from the scientific literature is illustrated and discussed. Results of numerical tests conducted on a noiseless as well as a noisy system will confirm that the algorithm discussed in the present paper performs in a satisfactory way. Also, the performances of the presented algorithm will be compared with those exhibited by other blind deconvolution algorithms known from the literature.

The correction of an adaptive optics (AO) system is always partially, a post-processing technique such as blind deconvolution can be used to improve the image quality. In this paper, we propose a blind deconvolution algorithm using Zernike-polynomial-based phase fitting to recover a high resolution and contrast image from the short-exposure AO images. The strategy is to parameterize the point spread function (PSF) with phase aberrations pixel by pixel and introduce a constraint to the phase estimations using phase fitting. The algorithm is robust and can lead to a restoration free of artifacts. We evaluate the performance of our proposed algorithm by a number of simulated experiments and it is well performed.

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.

There is high demand for deploying real-time or near real-time implementation of image reconstruction techniques for intelligence, surveillance, and reconnaissance (ISR) systems, as well as biomedical systems, and border applications. While many image restoration techniques have been implemented, their computational complexity makes them unsuitable for real-time systems. This work presents massively parallelized algorithms for two and three-dimensional image reconstruction of objects using deconvolution and bispectrum techniques. Speckle imaging (SI) techniques are more preferred for phase reconstruction because of its high restoration quality at the lower values of signal-to-noise ratio (SNR), especially for smaller targets, and its shift-invariant property which makes it insensitive to random motion of an object. This work proposes, a novel strategy for parallelization of recursive bispectrum algorithm. Furthermore, parallel bispectrum speckle imaging technique is used as an initial step for parallelized multi-frame blind deconvolution (MFBD) image restoration algorithm in order to speed-up the reconstruction process for anisoplanatic, long horizontal path imaging. The parallel hybrid Bispectrum- MFBD (B-MFBD) technique, through massive parallelization, provides significantly large improvement to both bispectrum SI and MFBD parts of the hybrid algorithm. The work also investigates phase diversity (PD) technique as the special case of MFBD algorithm, where the object is observed with a stack of intentionally defocused images. The known defocused information between phases is used to estimate the object and aberration function. The multi-plane PD was implemented for wide field fluorescence microscopy imaging of three-dimensional objects. The PD algorithm was subsequently used to generate 3D point cloud data for the image from its single view.

Blind deconvolution is a challenging problem, but in low-light it is even more difficult. Existing algorithms, both classical and deep-learning based, are not designed for this condition. When the photon shot noise is strong, conventional deconvolution methods fail because (1) the image does not have enough signal-to-noise ratio to perform the blur estimation; (2) While deep neural networks are powerful, many of them do not consider the forward process. When the noise is strong, these networks fail to simultaneously deblur and denoise; (3) While iterative schemes are known to be robust in the classical frameworks, they are seldom considered in deep neural networks because it requires a differentiable non-blind solver. This paper addresses the above challenges by presenting an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unsupervised</i> blind deconvolution method. At the core of this method is a reformulation of the general blind deconvolution framework from the conventional image-kernel alternating minimization to a purely kernel-based minimization. This kernel-based minimization leads to a new iterative scheme that backpropagates an unsupervised loss through a pre-trained non-blind solver to update the blur kernel. Experimental results show that the proposed framework achieves superior results than state-of-the-art blind deconvolution algorithms in low-light conditions.

Block-matching algorithm is the most common technique applied in block-based motion estimation technique. There are several block-matching algorithm based on block-based motion estimation techniques have been developed. Full search (FS), three step search (TSS), new three step search (NTSS), diamond search (DS) and hexagon based search (HS) are the most well known block-matching algorithm. These techniques are applied to video sequences to remove the temporal redundancy for compression purposes and to gauge the motion vector estimation. In addition, the mentioned block-matching algorithms are the baseline techniques that have been used to further develop all the enhanced or improved algorithms. In order to develop the proposed methods, the baseline techniques are studied to develop the proposed algorithms. This chapter proposes modelling of fast unrestricted hexagon search (FUHS16) and unrestricted hexagon-diamond search (UHDS16) algorithms for motion vector estimation, which is based on the theory and application of block-based motion estimation. Both of these algorithms are designed using 16 × 16 block size. In particular, the motion vector estimation, quality performance, computational complexity, and elapsed processing time are emphasised. These parameters have been used to measure the experimental results. It is the aim of this study that this work provides a common framework with which to evaluate and understand block-based matching motion estimation performance. On the theoretical side, four fundamental issues are explored: (1) division of frame, (2) basic block-based matching, (3) motion vector estimation, and (4) block-matching algorithm development. Various existing block-matching motion estimation algorithms have been analysed to develop the fundamental research. Based on the theoretical and fundamental research analysis the FUHS16 and UHDS16 algorithms using 16 × 16 block-based motion estimation formulations were developed. To improve the UHDS16 algorithm, 8 × 8 block-matching technique has been tested. The 8 × 8 block-matching technique is known as UHDS8. The results show positive improvements. From an application perspective, the UHDS8 algorithm efficiently captured the motion vectors in many video sequences. For example, in video compression, the use of motion vectors on individual macro-blocks optimized the motion vector information. The UHDS8 algorithm also offers improvement in terms of image quality performance, computational complexity and elapsed processing time. Thus, this chapter offers contributions in certain areas such as reducing the mechanism of computational complexity in estimating the motion from the video sequences. In particular, the FUHS16, UHDS16 and UHDS8 algorithms were developed to estimate the motion vectors field in the video sequences. Theoretical analysis block-based matching criteria are adapted to FUHS16, UHDS16 and UHDS8 algorithms, which are based on search points technique. Basically, the proposed of FUHS16, UHDS16 and UHDS8 algorithm produces the best motion vector estimation finding based on the block-based matching criteria. Besides that, the UHDS8 algorithm also improves the image quality performances and the search points in terms of the computational complexity. Overall, the study shows that the UHDS8 algorithm produces better results compared to the FUHS16 and UHDS16 algorithm.

In this paper, a method for MIMO blind deconvolution is proposed. The method is applicable to the case of non-Gaussian i.i.d. input signals. The core of the method is an algorithm which extracts one of the input signals from the information of the output signals only. This algorithm is an non-trivial generalization of the Shalvi-Weinstein algorithm for SISO blind deconvolution and converges in super-exponential rate possibly after finite iterations. By recursive use of this algorithm, extraction of all input signals can be achieved.

In order to alleviate the shortcomings of most blind deconvolution algorithms, this paper proposes an improved fast algorithm for blind deconvolution based on decorrelation technique and broadband block matrix. Althougth the original algorithm can overcome the shortcomings of current blind deconvolution algorithms, it has a constraint that the number of the source signals must be less than that of the channels. The improved algorithm deletes this constraint by using decorrelation technique. Besides, the improved algorithm raises the separation speed in terms of improving the computing methods of the output signal matrix. Simulation results demonstrate the validation and fast separation of the improved algorithm.

We propose to use the blind deconvolution and its modification to extract the point spread function (PSF) of layered metamaterials from a SNOM measurement. The measurement results are processed using the blind deconvolution algorithm to reconstruct the real-valued non-coherent PSF, or using the modified blind deconvolution introduced in this paper to reconstruct the complex-valued coherent PSF. The two algorithms are tested in simulations with a layered metamaterial deposited on a thin metallic mask with test apertures. We show that the modified algorithm is capable of recovering the approximate shape of complex PSF with a sub-wavelength full width at half maximum from a measurement in which the apertures are larger than the wavelength.

Single-image blind deconvolution is one of the most challenging fields in image processing which restores a sharp image from its blurred version. Nowadays blind deconvolution algorithms have made significant progress. However, the restoration of blurred images with little scale edges and periodic textures is still a hard work. To solve this problem, this paper proposes a new normalized sparse regularization blind deconvolution algorithm, which uses a gradient saliency map to prohibit the image small structures on image blurry kernel estimation. Firstly, salient detection is performed to select the important area which conforms with the human vision system and generates a binary mask to screen out useful gradients. Secondly, the normalized sparse regularization blind deconvolution method is applied to obtain accurate blur kernel and recover the sharp image. Finally, the experiment results show that the algorithm can effectively deblur the degraded image on different scenarios.

Dependent component analysis for blind restoration of images degraded by turbulent atmosphere

We enhance the performance of the nonnegativity and support constraints recursive inverse filtering (NAS-RIF) algorithm for blind image deconvolution. The original cost function is modified to overcome the problem of operation on images with different scales for the representation of pixel intensity levels. Algorithm resetting is used to enhance the convergence of the conjugate gradient algorithm. A simple pixel classification approach is used to automate the selection of the support constraint. The performance of the resulting enhanced NAS-RIF algorithm is demonstrated on various images.