A study of focus metrics and their application to automated focusing of inline transmission holograms

Burns, Nicholas; Watson, John

doi:10.1179/174313111x12966579709313

Cited by 13 publications

(5 citation statements)

References 7 publications

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“…In an earlier work, 18 the "Tenengrad" approach was shown to have some inherent weaknesses for holography of subsea organisms. This led us to investigate a new approach, dubbed "contour gradient," which was devised to overcome many of these difficulties.…”

Section: Focusing Techniques and The Contourmentioning

confidence: 99%

Robust particle outline extraction and its application to digital in-line holograms of marine organisms

Burns

Watson

2014

Opt. Eng

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Digital holography offers a method of high-resolution imaging of microscopic particles and organisms in their natural environment. Automated image extraction and data processing are essential for rapid interrogation and analysis of the vast amounts of information contained in a typical hologram. In this work, we describe a robust-automated particle focusing approach, which we have developed to extract outlines of all particles contained within the sampling volume of each hologram constituting a "holovideo." The output data consists of ordered point-lists delineating polygons that match particle outlines and facilitate further processing such as extraction of focused images from the holograms themselves. The algorithm developed allows the reduction of, typically, a 2-GB holovideo to tens of megabytes, thereby greatly reducing analysis time by allowing rapid scanning of the contoured images without manual focusing. The algorithm has been demonstrated on synthetic and laboratory holograms and applied to holographic videos recorded in the North Sea. The algorithm output also lends itself to further automated analysis techniques like particle tracking or automated recognition.

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Section: Focusing Techniques and The Contourmentioning

confidence: 99%

Robust particle outline extraction and its application to digital in-line holograms of marine organisms

Burns

Watson

2014

Opt. Eng

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“…In order to determine the degradation range of r and λ, we constantly compared the evaluation scores between the generated degraded images and the real observed images by using two non-reference image quality evaluation methods (Variance and Brenner) [85]. Eventually, the ranges of these two variables are set to (r x , r y ) and (λ x , λ y ).…”

Section: How To Generate Training Datamentioning

confidence: 99%

Multi-Frame Blind Restoration for Image of Space Target With FRC and Branch-Attention

2020

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The random noise and anisotropic motion of atmospheric turbulence can cause different degradation patterns, which make images of space targets observed from ground-based stations severely disturbed. In recent years, benefit from the development of convolutional neural networks (CNNs), a large number of effective end-to-end methods were proposed to restore images. However, a single-frame method whose input is just a single image can hardly achieve a further improvement for the restoration image due to the diversified degradation patterns of space-target images. In this paper, we proposed a multi-branch network with a multi-frame input to restore space-target images. The multi-frame input contains spacetarget images which own different degradation patterns at different moments. In this way, we can fully use the complementary information between input frames. And in this network, two effective technologies are introduced: one is the full resolution convolution module which extracts features by using convolutional layers with different dilation rates to keep feature information complete; the other is the branch-attention module which is used to pass effective information between different branches of the network. Furthermore, we demonstrated the effectiveness of our method by comparing it with those state-of-the-art methods. INDEX TERMS Multi-frame image restoration, multi-branch network, self-attention, branch-attention, full resolution convolution, images of space targets.

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“…Many of the existing algorithms have been found to be susceptible to the speckle noise encountered in coherent imaging, and to produce false maximums due to combination of diffraction orders from complex particle shapes or distributions. Of these metrics, Tenengrad has been seen to offer the best results [15] and the contour gradient algorithm can be considered a modification of the Tenengrad algorithm. Aside from increased noise immunity [14], a further major benefit of the contour gradient algorithm in this work is that its output is a set of contours, identifying particle contours.…”

Section: Overviewmentioning

confidence: 99%

“…Of these metrics, Tenengrad has been seen to offer the best results [15] and the contour gradient algorithm can be considered a modification of the Tenengrad algorithm. Aside from increased noise immunity [14], a further major benefit of the contour gradient algorithm in this work is that its output is a set of contours, identifying particle contours. These contours offer a good first guess for the positioning of point sources prior to execution of the iterative least-squares minimization.…”

Section: Overviewmentioning

confidence: 99%

“…This approach is based on an iterative numerical inversion of holograms through constrained nonlinear optimization. We describe a technique whereby an initial guess of an object's position and contour is obtained using a particular coarse focus metric known as Contour Gradient [14] which has been seen to perform better than alternatives such as Tenengrad [15]. Subsequently, the minimization is performed on point sources located at positions around the optimal contour detected by this algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Least-squares based inverse reconstruction of in-line digital holograms

Kamau

Burns

Falldorf

et al. 2013

J. Opt.

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We present a least-squares solution for the inverse problem in in-line digital holography which is based on a point source model. We demonstrate that by reformulating the reconstruction problem as an inverse problem and by integrating a contour gradient based auto-focus search algorithm into the reconstruction routine, a more fundamental solution for the inversion of a hologram can be attained. With this approach the inversion can be calculated without any prior knowledge of the object’s shape/size and without imposing any constraints on the imaging system. In a proof-of-concept study we show that our method facilitates a more accurate reconstruction, as compared to conventional methods, and that it facilitates object localization with an accuracy on the order of the optical wavelength.

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A study of focus metrics and their application to automated focusing of inline transmission holograms

Cited by 13 publications

References 7 publications

Robust particle outline extraction and its application to digital in-line holograms of marine organisms

Robust particle outline extraction and its application to digital in-line holograms of marine organisms

Multi-Frame Blind Restoration for Image of Space Target With FRC and Branch-Attention

Least-squares based inverse reconstruction of in-line digital holograms

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