PARALLEL IMPLEMENTATION OF BISPECTRUM MULTI-FRAME DECONVOLUTION FOR IMAGE RECONSTRUCTION

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.

The phase diversity (PD) algorithm is an image-based co-phasing error detection method for segmented mirror synthetic aperture optical systems. Particle swarm optimization (PSO) is suitable for PD for its fast convergence speed and simple structure. However, with the increase of the numbers of subapertures, optimization of cost function formed by the PD algorithm becomes a high-dimension non-linear optimization problem, which would lead PSO to result in a premature solution. Regarding the problem above, this paper presented a modified PSO in the PD algorithm to co-phase the segmented primary mirror, which overcame drawbacks of the traditional PSO, such as premature convergence and deactivation of particles, and enhanced the dig ability of the algorithm. Vast simulation results show that the modified PSO in the PD algorithm can effectively restore the segmented primary mirror, reducing the peak-to-valley (PV) value to 0.0012λ and the root mean square error to 0.0007λ, and raising the Strehl ratio to over 0.999. A comparison was also conducted to show that the modified PSO has advantages over two existing algorithms in higher accuracy and faster convergence speed.

Phase diversity experimental results: deconvolution of ν Scorpii

We compare the performance of four metrics for use in a phase diversity algorithm. Three of the metrics utilize a regularization based on the signal-to-noise ratio.

A theoretical description of Phase Diversity is done in some detail to lay the foundations for the experimental effort. The Phase Diversity algorithm is formulated in the context of nonlinear programming where a metric is developed and then minimized. This development will show how the Zernike coefficients can be solved for directly using nonlinear optimization techniques. Computer simulations are used to validate the algorithms and techniques. The results of the computer simulations are shown. Once the confidence of the algorithms and techniques are established in the computer simulations, they are used on actual laboratory data. Detailed discussion of the laboratory implementation will be described and the laboratory results will be shown.

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.

Imaging research on complex samples going through random scattering medias is a very difficult challenge in the field of computational optics. With the deep learning methods applying to the field of speckle correlation image reconstruction, many problems of imaging through strong scattering media and small perturbations of the media which reduces the imaging performance have been resolved. However, due to the randomness of the scattering medium, large and complex samples are required to be trained, deep learning based methods alway occupy a lot of energy consumption on Graphic Processing Unit (GPU). Cyber-Physical-Social Systems (CPSS) integrating the cyber, physical, and social worlds is a key technology to provide proactive and personalized services for humans. This paper proposes an efficient CNN to do speckle image reconstruction based on cloud-edge computing for CPSS, which can achieve higher image resolution by less inputs.In this work, we design a self-back stacked Efficient Residual Factorized Network (SBS-ERFNet) to do image reconstruction through scattering medium. Different from the traditional ERFNets, our framework includes two stages of training, we use this model to study the speckle image from a low-to-high resolution manner. The experiments show that even if a small samples is input for training, the test results can reach a high resolution.

The misalignment between recorded in-focus and out-of-focus images using the Phase Diversity (PD) algorithm leads to a dramatic decline in wavefront detection accuracy and image recovery quality for segmented active optics systems. This paper demonstrates the theoretical relationship between the image misalignment and tip-tilt terms in Zernike polynomials of the wavefront phase for the first time, and an efficient two-step alignment correction algorithm is proposed to eliminate these misalignment effects. This algorithm processes a spatial 2-D cross-correlation of the misaligned images, revising the offset to 1 or 2 pixels and narrowing the search range for alignment. Then, it eliminates the need for subpixel fine alignment to achieve adaptive correction by adding additional tip-tilt terms to the Optical Transfer Function (OTF) of the out-of-focus channel. The experimental results demonstrate the feasibility and validity of the proposed correction algorithm to improve the measurement accuracy during the co-phasing of segmented mirrors. With this alignment correction, the reconstructed wavefront is more accurate, and the recovered image is of higher quality.

This paper presents the results of experimental study of full field laser speckle imaging due to cortex microcirculation state monitoring for laboratory rats under conditions of stroke and the introduction of agents. Three groups of experimental animals from five animals in each group were studied. The behavior of blood flow, studied by speckle imaging technique, matched the expected physiological response to an impact.

Underwater computer vision has attracted increasing attention in the research community due to the recent advances in underwater platforms such as of rovers, gliders, autonomous underwater vehicles (AUVs) , and the like, that now make possible the acquisition of vast amounts of imagery and video for applications such as biodiversity assessment, environmental monitoring, and search and rescue. Despite growing interest, underwater computer vision is still a relatively under-researched area, where the attention in the literature has been paid to the use of computer vision techniques for image restoration and reconstruction, where image formation models and image processing methods are used to recover colour corrected or enhanced images. This is due to the notion that these methods can be used to achieve photometric invariants to perform higher-level vision tasks such as shape recovery and recognition under the challenging and widely varying imaging conditions that apply to underwater scenes. In this paper, we review underwater computer vision techniques for image reconstruction, restoration, recognition, depth, and shape recovery. Further, we review current applications such as biodiversity assessment, management and protection, infrastructure inspection and AUVs navigation, amongst others. We also delve upon the current trends in the field and examine the challenges and opportunities in the area.

Increasing the resolution of the MRI scans results in lowering the signal-to-noise ratio and/or increasing the scan time. In this paper, we improved the resolution of MRI images by obtaining a high resolution image from a sequence of low resolution images using the super-resolution reconstruction methodology. Image restoration is an important step in the reconstruction process, the final appearance and the quality of the reconstructed image depend greatly on the restoration method used. In this paper, we evaluated the performance of three restoration techniques used in the reconstruction, the first one is based on truncated singular value decomposition, the second one is based on Tikhonov regularization and the last one is based on total variation. Experiments are performed on synthetic images and real MRI brain images to verify the results obtained from the numerical simulations.

Image reconstruction through a hollow core fiber via deep learning

Optical Coherence Tomography (OCT) is often employed to observe the retinal layers in the human eyes. The retinal scans are susceptible to artefacts such as head movements or eye blinks. Along with this, the quality of the images is degraded by speckle noise caused due to the constructive and destructive interference of the waves used for capturing data. Recently, image restoration techniques have geared up in terms of quality with the exertion of deep learning. Despeckling using deep learning, in general, necessitates a large set of training images. On the contrary, deep image prior is a novel model that performs denoising operations using a single training image, based on a prior assumption about the noise distribution. This paper extends the concept of the deep image prior towards non-local restoration for speckle noise assuming that the speckle follows Gamma distribution. Such a framework can be incorporated to enhance the OCT images. The proposed framework is assessed qualitatively with visual comparisons and quantitatively using statistical measures like PSNR, CNR and ENL. Comparative studies confirm that the proposed method outperforms the existing methods in restoring speckled input images.

Adaptive optics (AO) can efficiently compensate for turbulence-induced distortion in orbital angular momentum (OAM)-based optical wireless communication (OWC) systems. In this paper, we design a modified phase diversity algorithm (MPDA)-based wavefront sensor to enhance the reconstruction accuracy of distorted OAM wavefront information. Aiming to further strike a compelling trade-off between AO system complexity and compensation accuracy, we first construct a novel AO system that applies a quickly and electronically controlled focus-tunable lens (FTL). It decontaminates distorted OAM signaling beams while having a low systemic complexity and superior convergence performance. Furthermore, we propose the 3-modified phase diversity algorithm (3-MPDA) AO scheme relying upon a Fourier intensity and two defocused intensities as the prior information, which beneficially balances the compensation effect and the number of defocused intensities and exhibits good noise robustness against charge-coupled device (CCD) detectors. In summary, this paper provides new insight for designing AO schemes with high compensation performance in communication links.

相位差异法探测波前的误差分析及消除方法