Defocused Images Research Articles

Defocused Image Changes Signaling of Ganglion Cells in the Mouse Retina.

Myopia is a substantial public health problem worldwide. Although it is known that defocused images alter eye growth and refraction, their effects on retinal ganglion cell (RGC) signaling that lead to either emmetropization or refractive errors have remained elusive. This study aimed to determine if defocused images had an effect on signaling of RGCs in the mouse retina. ON and OFF alpha RGCs and ON–OFF RGCs were recorded from adult C57BL/6J wild-type mice. A mono green organic light-emitting display presented images generated by PsychoPy. The defocused images were projected on the retina under a microscope. Dark-adapted mouse RGCs were recorded under different powers of projected defocused images on the retina. Compared with focused images, defocused images showed a significantly decreased probability of spikes. More than half of OFF transient RGCs and ON sustained RGCs showed disparity in responses to the magnitude of plus and minus optical defocus (although remained RGCs we tested exhibited similar response to both types of defocus). ON and OFF units of ON–OFF RGCs also responded differently in the probability of spikes to defocused images and spatial frequency images. After application of a gap junction blocker, the probability of spikes of RGCs decreased with the presence of optical defocused image. At the same time, the RGCs also showed increased background noise. Therefore, defocused images changed the signaling of some ON and OFF alpha RGCs and ON–OFF RGCs in the mouse retina. The process may be the first step in the induction of myopia development. It appears that gap junctions also play a key role in this process.

Multiple parametric nanoscale measurements with high sensitivity based on through-focus scanning optical microscopy.

High-throughput through-focus scanning optical microscopy (TSOM) involves defocusing along the optical axis and capturing a series of defocus images and is useful in optical nanoscale measurement. However, TSOM is usually affected by its optical and mechanical noises. In this study, the issue of sensitivity and application in three-dimensional (3D) multiple parameter measurement of TSOM is investigated. First, a TSOM system with objective scanning and its relative simulation algorithm are proposed. Second, based upon the system and algorithm, an experiment on an isolated Au line is performed and the corresponding matching library is established. Comparing the experimental TSOM image and simulated TSOM images of the library, 3D multiple parameter results of the Au line are extracted. Third, the precision of the system is analysed through a fidelity test particular for through-focus images. According to this study, the system is robust to the optical and mechanical noises and hence could be useful in 3D multiple parametric measurement and high-volume nanomanufacturing.

Modeling and Quantitative Analysis of Tropospheric Impact on Inclined Geosynchronous SAR Imaging

Geosynchronous orbit synthetic aperture radar (GEO SAR) has a long integration time and a large imaging scene. Therefore, various nonideal factors are easily accumulated, introducing phase errors and degrading the imaging quality. Within the long integration time, tropospheric status changes with time and space, which will result in image shifts and defocusing. According to the characteristics of GEO SAR, the modeling, and quantitative analysis of background troposphere and turbulence are conducted. For background troposphere, the accurate GEO SAR signal spectrum, which takes into account the time-varying troposphere, is deduced. The influences of different rates of changing (ROC) of troposphere with time are analyzed. Finally, results are verified using the refractive index profile data from Fengyun (FY) 3C satellite and the tropospheric zenith delays data from international GNSS service (IGS). The time–space changes of troposphere can cause image shifts which only depend on the satellite beam-foot velocity and the linear ROC of troposphere. The image defocusing is related to the wavelength, resolution requirement, and the second and higher orders of ROC. The short-wavelength GEO SAR systems are more susceptible to impacts, while L-band GEO SAR will be affected when the integration time becomes longer. Tropospheric turbulence will cause the amplitude and phase random fluctuations resulting in image defocusing. However, in the natural environment, radio waves are very weakly affected by turbulence, and the medium-inclined GEO SAR of L- to C-band will not be affected, while the X-band will be influenced slightly.

FPGA Design of Real-Time MDFD System Using High Level Synthesis

3D shape information is one of the very important clues in image processing and computer vision. Unlike traditional multi-input depth from defocus (DFD) technique, monocular DFD (MDFD) algorithm proposed by Hu and Haan can reconstruct 3D shape only from a single monocular defocus image with low computing complexity. In this paper, we present a real-time MDFD system implemented on the FPGA device. In order to reduce the FPGA design cost, vivado high level synthesis (VHLS) is applied to design the MDFD system. The system architecture on the basis of FIFO based convolution is first designed through C/C++ code that is further converted to the FPGA design by VHLS. Then the PIPELINE, LOOP_MERGE, and ARRAY_PARTITION directives are used to optimize the latency and interval of the proposed system. The performance and resource utilization of the whole system are evaluated by processing defocus images from the real scene with 640×480 pixel size. The system can process about 22 images at 20 MHz working frequency and keep the 93.29% depth accuracy on the 3D objects test, which achieves a real-time state-of-the-art MDFD system by comparing to other recent works.

一种用于评价聚焦形貌恢复算法的图像离焦仿真技术

一种用于评价聚焦形貌恢复算法的图像离焦仿真技术

Defocus Fuzzy Image Restoration

In daily life, if the focus is not on the object to be photographed, the image is blurred. This is the blurred image caused by the defocus. The defocus is a very common factor, and in most cases, the defocus amount is unpredictable, which brings great difficulty to defocus image restoration. In the existing defocus fuzzy image restoration method, the defocused point diffusion function can be estimated by determining the model parameters of the defocus blurred image, and then the reconstructed image can be obtained by inverse filtering or Wiener filtering. This paper introduces the causes of the defocus image, expounds the inverse filtering, Wiener filtering and blind restoration methods, and analyses the different principles of the three methods, and uses inverse filtering, Wiener filtering and blind restoration to deal with the defocus blurred images, and the expected results are obtained.

SAR Scene Size Limits for Spatially Invariant Defocus With Near- and Far-Field Geometries

Existing traditional or phase-only autofocus techniques rely on the assumption that the phase-error function, which causes synthetic aperture radar (SAR) image defocus, is spatially invariant. In many cases this assumption holds, but for larger scene sizes or scenarios where the far-field approximation does not hold, the spatially invariant phase error approximation breaks down. In this paper, an approximate upper bound is derived on the spatially variant phase-error component of the phase-error function. The derived bound applies with near- and far-field bistatic geometries, with monostatic as a special case, and is expressed in terms of either known quantities or quantities which typically have a known upper bound. The derived bound enables SAR scene size limits to be computed, such that the phase-error function is approximately spatially invariant according to a specified phase threshold. Example results identifying SAR scene size limits for spatially invariant defocus are presented for both near- and far-field geometries.

An ISAR Imaging Algorithm for Nonuniformly Rotating Targets With Low SNR Based on Modified Bilinear Parameter Estimation of Cubic Phase Signal

For nonuniformly rotating target in the low signal-to-noise ratio (SNR) environment, the inverse synthetic aperture radar (ISAR) imaging is a challenging task due to the Doppler spread induced by time-varying rotation. In this paper, an effective modified bilinear parameter estimation algorithm is proposed and applied to reconstruct the ISAR image of nonuniformly rotating targets with low SNR. First, azimuth echoes of a range cell are modeled as multicomponent cubic phase signals (multi-CPSs) after range alignment and phase adjustment. Then, to estimate the parameter of the CPS (the chirp rate and the quadratic chirp rate, which are identified as causes of the target image defocus), a novel integrated parametric cubic phase function (IPCPF) and a reversing Wigner–Ville distribution processing are developed. Compared to other four representative algorithms, the bilinearity of the IPCPF guarantees a higher antinoise performance and a better suppression on cross terms. Moreover, by utilizing the nonuniform fast Fourier transform and the generalized scaled Fourier transform, the brute-force searching is eliminated and the computational cost is reduced. Finally, a new cross-range scaling method based on a regression analysis is proposed for the ISAR imaging. With the synthetic data and the real radar data, several simulation examples and ISAR imaging results demonstrate the effectiveness and the superiority of the proposed ISAR imaging algorithm.

Feature-based phase retrieval wavefront sensing approach using machine learning.

A feature-based phase retrieval wavefront sensing approach using machine learning is proposed in contrast to the conventional intensity-based approaches. Specifically, the Tchebichef moments which are orthogonal in the discrete domain of the image coordinate space are introduced to represent the features of the point spread functions (PSFs) at the in-focus and defocus image planes. The back-propagation artificial neural network, which is one of most wide applied machine learning tool, is utilized to establish the nonlinear mapping between the Tchebichef moment features and the corresponding aberration coefficients of the optical system. The Tchebichef moments can effectively characterize the intensity distribution of the PSFs. Once well trained, the neural network can directly output the aberration coefficients of the optical system to a good precision with these image features serving as the input. Adequate experiments are implemented to demonstrate the effectiveness and accuracy of proposed approach. This work presents a feasible and easy-implemented way to improve the efficiency and robustness of the phase retrieval wavefront sensing.

Fast detection and segmentation of partial image blur based on discrete Walsh–Hadamard transform

Image signals can be blurred due to defocus or motion. Blur may be undesirable for image sensing, but may also contain useful information. Therefore, detecting the blurriness of each pixel and segmenting the partial blur regions in natural images are important and yet challenging in the field of machine vision. A concise no-reference method based on discrete Walsh–Hadamard transform is proposed to detect and segment partial blur in this paper. First, a re-blurring strategy is performed over multiple overlapping image patches extracted from the test image. Then, for both test image and re-blurred image, discrete Walsh–Hadamard transforms are utilized in each image patches to obtain the blur map. This blur map can characterize the blurriness of each pixel in test image. Based on it, combined with K-Means clustering and region-growing, the test image can be segmented into blurry/non-blurry regions. The experiments, performed on a public dataset, demonstrate the capability of the proposed metric in the detection and segmentation of the blur region. Comparative results with the state-of-the-art show the superiority of the proposed approach in image segmentation for both defocus and motion blur images. The proposed approach is compendious without data training and possesses a high time efficiency because of the fast sequency transform.

Depth Estimation from Defocused Images: A Survey

<p>An important step in 3D data generation is the generation of depth map. Depth map is a black and white image which has exactly the same size of the original captured 2D image that indicates the relative distance of each pixel from the observer to the objects in the real world. This paper presents a survey of Depth Perception from Defocused or blurs images as well as image from motion. The change of distance of the object from the camera has direct relation with the amount of blurring of object in the image. The amount of blurring will be calculated with a comparison in front of the camera directly and can be seen with the changes at gray level around the edges of objects.</p>

Lens magnification affects the estimates of refractive error obtained using eccentric infrared photorefraction.

Positive- and negative-powered ophthalmic lenses are used in eccentric infrared photorefraction to calibrate the device, correct the subject's baseline refractive error before an experimental manipulation, or stimulate blur-driven accommodation. Through theoretical modeling of luminance gradients formed across the pupil and empirical measurements of the eye's refractive error using a commercial photorefractor, this study shows that image magnification by positive lenses and image minification by negative lenses under- and overestimates the refractive error, respectively, all independent of image defocus. The impact of image magnification/minification therefore appears non-trivial in experimental paradigms involving ophthalmic lenses to manipulate the eye's optics during photorefraction.

Phase retrieval using through-focus images in Lorentz transmission electron microscopy.

In this study, we report on phase retrieval by the maximum likelihood method with conjugate gradient method (CG-MAL) using through-focus images in Lorentz transmission electron microscopy (LTEM). The method was evaluated using 32 simulated and experimentally obtained through-focus images of magnetic bubbles; these images were collected under a defocus range from approximately -3 mm to 3 mm. Consequently, we obtained the magnetic domain structures of the magnetic bubbles in both the simulation and the LTEM experiment. Furthermore, the CG-MAL method showed better convergence behavior than other iterative phase retrieval methods. Therefore, the method can also be widely and effectively applied to the observation of magnetic domain structures other than magnetic bubbles when highly defocused through-focus images are used.

Temporal-Spatial Varying Background Ionospheric Effects on the Moon-Based Synthetic Aperture Radar Imaging: A Theoretical Analysis

The Moon-based synthetic aperture radar (SAR) potentially offers an unprecedented high temporal resolution and wide spatial coverage, simultaneously, for earth observation. However, the complex geometry in relating Moon-SAR-Earth motion, where both the earth’s rotation and lunar revolution exert, poses a great challenge in signal processing and image formation. Besides, the background ionospheric effects contributing to the phase dispersion become even more critical because the ionospheric freezing model usually assumed in the low earth orbit SAR imaging is no longer valid for the Moon-based SAR, and the curved trajectory induced by the ultra-long synthetic aperture time further complicates the background ionospheric effects. In this paper, we investigate such effects on the Moon-based SAR imaging by establishing an accurate curved trajectory signal model. The image shift and image defocus caused by the temporal–spatial varying background ionosphere are evaluated in detail. Numerical analysis shows that the range shift can be up to hundreds of meters, while the azimuth shift is much less, on the order of tens of meters. As for image quality, the range defocusing is persistently significant, and yet the azimuth focusing is only slightly disturbed. Under the presence of background ionosphere, the geometric distortion and image defocusing given rise to a Moon-based SAR deserve a special care in the context of image quality.

基于光强传输方程与SLM的快速相位恢复

The transport of intensity equation(TIE) offers an experimentally simple technique for computing phase information directly from several defocused images. In this work we developed the traditional TIE intensity acquisition system. In order to avoid the shifting of CCD, a quadratic phase pattern was displayed on the SLM to provide a lens effect for realizing different defocus distance by varying the focal length of the lens. Two kinds of phase imaging experimental configurations guided by two different theories were designed. In most instances, since a camera is used to capture images in which the phase modulation of lens cannot be ignored, the former intensity acquisition system was designed based on the single lens optical propagation system, and the relationship between the focal length and the image distance in the lens law was used to facilitate the varying of the defocus distance by changing the phase pattern displayed on the SLM. The latter was designed by locating a SLM in the Fourier domain of the 4f setup, according to the Fourier transform property of the lens and the Fresnel diffraction theory, the relationship between the defocus distance and the focal length of the quadratic phase pattern can be derived. The experimental results verify that the proposed phase retrieval methods are reasonable and correct.

Super-Resolution Reconstruction From Multiple Defocused Infrared Images of Stationary Scene

Infrared image has poor visual effect for its low resolution. Super-resolution reconstruction (SRR) is an effective means to address this problem. Existing SRR algorithms use well-focused images and ignore the value of defocused images generated by the infrared imaging system during focusing. The basic idea of the present study is to treat a defocused infrared image as distribution and accumulation of scene information among different pixels of the infrared detector, as well as a valid observation of the imaged subject; defocused images are the result of blurring a corresponding high resolution (HR) image using a point spread function (PSF) followed by downsampling. From this idea, we used multiple defocused images to build an observation model for HR images and propose an SRR algorithm to approach the HR images. We have developed an image degradation model by analyzing optical lens imaging, using the particle swarm optimization algorithm to estimate the PSF of the HR image, and using compressed sensing theory to implement SRR based on the noncoherent characteristics of the defocused infrared images. Experiments demonstrate that our method can be used to obtain more information about details of a scene and improve the visual effect without adding any hardware facilities, improving the recognition and interpretation of the image subject.

Revealing the Mechanism of Photoluminescence from Single Gold Nanospheres by Defocused Imaging

The mechanism for the photoluminescence (PL) emission from gold nanoparticles has attracted considerable attention for many years. However, there is an important gap between small nanoclusters (∼2 nm) and larger plasmonic particles (∼50 nm). In this work, using defocused imaging technique, we investigate the PL properties of gold nanospheres (15–20 nm in diameter) on a single-particle level. Photoblinking and photobleaching phenomena are both observed. We notice that although these nanospheres can support surface plasmon resonance at ∼515 nm, they emit at ∼630 nm (excited by 532 nm), which is obviously plasmon-independent. The observed defocused images (DIs) exhibit isotropy first and then either transform into anisotropy or vanish rapidly. Surprisingly, the DIs can change their emission pattern within one single nanosphere during the tracing time. All these PL properties suggest a multiple-dipole-emission model. High-resolution transmission electron microscopy images demonstrate that these nanospheres are polycrystalline containing multiple small crystal domains (∼3 nm). We believe that these small domains divide the particle into different clusters and give rise to the photoblinking behavior and rotary DIs. The presented PL mechanism links gold nanoclusters and single-crystal nanorods, which could be helpful in understanding the origins of photoblinking and the luminescent properties from metallic nanoparticles. In addition, these water-soluble gold nanospheres provide new opportunities for biological labels and light-emitting sources in nanophotonics.

Positioning a fluorescent probe at the core of a glassy star polymer for detection of local dynamics

Accessing local dynamics within a single macromolecule is the key to understand the physical origin of the viscoelasticity and especially the glass transition. In order to extract specific information on the dynamics of the branch point of a star polymer around its glass transition temperature, four-arm star poly (n-butyl methacrylate) with a fluorescent core was synthesized using perylene diimide as initiator and polymerization conducted via atom transfer radical polymerization. The process is found to be effective in positioning the fluorophore at the branch point with the fluorophore intact, which allows the successful application of single molecule fluorescence defocus imaging in examining the local site-sensitive dynamics. The power spectra of rotation trajectories, the population of rotating fluorophores as well as the distribution of angular displacement were used to revel the difference in local dynamics between branch point and the arm’s end. It is discovered that the local dynamics at the core of the star polymer is much less activated than that at the arm’s end. The results demonstrate the strong effect dues to the topological constrain at the branch point and the more free space at the arm’s end.

Calibrated brightfield-based imaging for measuring intracellular protein concentration.

Intracellular protein concentration is an essential cell characteristic, which manifests itself through the refractive index. The latter can be measured from two or more mutually defocused brightfield images analyzed using the TIE (transport-of-intensity equation). In practice, however, TIE does not always achieve quantitatively accurate results on biological cells. Therefore, we have developed a calibration procedure that involves successive imaging of cells in solutions containing different amounts of added protein. This allows one to directly relate the output of TIE (T) to intracellular protein concentration C (g/L). The resultant relationship has a simple form: C ≈ 1.0(T/V), where V is the cell volume (μm3 ) and 1.0 is an empirical coefficient. We used calibrated TIE imaging to characterize the regulatory volume increase (RVI) in adherent HeLa cells placed in a hyperosmotic solution. We found that while no RVI occurs over the first 30-60 min, the protein concentration fully recovers after 20 h. Because interpretation of such long experiments may depend on whether protein concentration varies significantly throughout the cell cycle, we measured this parameter in three cell lines: HeLa, MDCK and DU145. Our data indicate that protein concentration remains relatively stable in these cells. © 2017 International Society for Advancement of Cytometry.

Nanoparticle classification in wide-field interferometric microscopy by supervised learning from model.

Interference-enhanced wide-field nanoparticle imaging is a highly sensitive technique that has found numerous applications in labeled and label-free subdiffraction-limited pathogen detection. It also provides unique opportunities for nanoparticle classification upon detection. More specifically, the nanoparticle defocus images result in a particle-specific response that can be of great utility for nanoparticle classification, particularly based on type and size. In this work, we combine a model-based supervised learning algorithm with a wide-field common-path interferometric microscopy method to achieve accurate nanoparticle classification. We verify our classification schemes experimentally by blindly detecting gold and polystyrene nanospheres, and then classifying them in terms of type and size.