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With the rapid development of synthetic aperture radar (SAR), delivery platforms are gradually becoming diversified and miniaturized. The SAR flight process is susceptible to external influences, resulting in unsatisfactory imaging results, so it is necessary to optimize imaging processing in combination with the SAR imaging quality assessment (IQA) index. Based on the principle of SAR imaging, this paper analyzes the impact of defocusing on imaging results caused by mismatched filters and draws on the assessment algorithm of motion blur, proposing a SAR IQA index based on average blurred edge width (ABEW) in the salient area. In addition, the idea of decomposing the transfer function in the frequency domain and fitting the matched filter with a polynomial is also proposed. The estimation of the flight trajectory is changed to a correction of the matched filter, avoiding the precise estimation of Doppler parameters and complex calculations during the time–frequency conversion process. The effectiveness of ABEW was verified by using SAR images of real scenes, and the results were highly consistent with the actual image quality. The imaging processing was tested using the echo signals generated by the errors introduced during the flight process, and more satisfactory imaging results were obtained by using ABEW with the filter for correction. The imaging process was tested using the echo signal generated by introducing errors during the flight, and the filter was corrected using ABEW as an index, obtaining a comparatively ideal imaging result. [ABSTRACT FROM AUTHOR]

Defocus is an important factor that causes image quality degradation of optoelectronic tracking equipment in the shooting range. In this paper, an improved blind/referenceless image spatial quality evaluator (BRISQUE) algorithm is formulated by using the image characteristic extraction technology to obtain a characteristic vector (CV). The CV consists of 36 characteristic values that can effectively reflect the defocusing condition of the corresponding image. The image is evaluated and scored subjectively by the human eyes. The subjective evaluation scores and CVs constitute a set of training data samples for the defocusing evaluation model. An image database that contains sufficiently many training samples is constructed. The training model is trained to obtain the support vector machine (SVM) model by using the regression function of the SVM. In the experiments, the BRISQUE algorithm is used to obtain the image feature vector. The method of establishing the image definition evaluation model via SVM is feasible and yields higher subjective and objective consistency. [ABSTRACT FROM AUTHOR]

Plasma sheath poses a serious challenge to inverse synthetic aperture radar (ISAR) imaging of hypersonic targets. This paper investigated the distribution characteristics of the electron density and velocity field in the plasma sheath surrounding the hypersonic target in various flight scenes. The incident depth and reflective surface of electromagnetic (EM) waves with X-band, Ku-band, and Ka-band can be determined based on the plasma frequency. We established the echo model coupled with the velocity field of the plasma sheath on the reflective surface and obtained one-dimensional range profiles and ISAR images of the hypersonic target in various flight scenes. The simulation results indicated that the non-uniform velocity field on the reflective surface induced displacement and diffusion in the one-dimensional range profile, resulting in ISAR image distortion. A changing flight scene and radar frequency can have an impact on imaging results. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) were utilized to assess the impact of plasma sheath on ISAR images. This study revealed the defocus mechanism of the ISAR image caused by the velocity field of the plasma sheath and provided a theoretical reference for the selection of radar frequency for hypersonic targets in various flight scenes. [ABSTRACT FROM AUTHOR]

Different from the object motion blur, the defocus blur is caused by the limitation of the cameras’ depth of field. The defocus amount can be characterized by the parameter of point spread function and thus forms a defocus map. In this paper, we propose a new network architecture called Defocus Image Deblurring Auxiliary Learning Net (DID-ANet), which is specifically designed for single image defocus deblurring by using defocus map estimation as auxiliary task to improve the deblurring result. To facilitate the training of the network, we build a novel and large-scale dataset for single image defocus deblurring, which contains the defocus images, the defocus maps and the all-sharp images. To the best of our knowledge, the new dataset is the first large-scale defocus deblurring dataset for training deep networks. Moreover, the experimental results demonstrate that the proposed DID-ANet outperforms the state-of-the-art methods for both tasks of defocus image deblurring and defocus map estimation, both quantitatively and qualitatively. The dataset, code, and model is available on GitHub: https://github.com/xytmhy/DID-ANet-Defocus-Deblurring. [ABSTRACT FROM AUTHOR]

Depth of focus is the major contributor to lithographic process margin. One of the major causes of focus variation is imperfect planarization of fabrication layers. Presently, optical proximity correction (OPC) methods are oblivious to the predictable nature of focus variation arising from wafer topography. As a result, designers suffer from manufacturing yield loss as well as loss of design quality through unnecessary guardbanding. In this paper, the authors propose a novel flow and method to drive OPC with a topography map of the layout that is generated by chemical-mechanical polishing simulation. The wafer topography variations result in local defocus, which the authors explicitly model in the OPC insertion and verification flows. In addition, a novel topography-aware optical rule check to validate the quality of result of OPC for a given topography is presented. The experimental validation in this paper uses simulation-based experiments with 90-nm foundry libraries and industry-strength OPC and scattering bar recipes. It is found that the proposed topography-aware OPC (TOPC) can yield up to 67% reduction in edge placement errors. TOPC achieves up to 72% reduction in worst case printability with little increase in data volume and OPC runtime. The electrical impact of the proposed TOPC method is investigated. The results show that TOPC can significantly reduce timing uncertainty in addition to process variation. [ABSTRACT FROM AUTHOR]

Camera parameters can't be estimated accurately using traditional calibration methods if the camera is substantially defocused. To tackle this problem, an improved approach based on three phase-shifting circular grating (PCG) arrays is proposed in this paper. Rather than encoding the feature points into the intensity, the proposed method encodes the feature points into the phase distribution, which can be recovered precisely using phase-shifting methods. The PCG centers are extracted as feature points, which can be located accurately even if the images are severely blurred. Unlike the previous method which just uses a single circle, the proposed method uses a concentric circle to estimate the PCG center, such that the center can be located precisely. This paper also presents a sorting algorithm for the detected feature points automatically. Experiments with both synthetic and real images were carried out to validate the performance of the method. And the results show that the superiority of PCG arrays compared with the concentric circle array even under severe defocus. [ABSTRACT FROM AUTHOR]

As advanced technologies in wafer manufacturing push patterning processes toward lower k1 subwavelength printing, lithography for mass production potentially suffers from decreased patterning fidelity. This results in the generation of many hotspots, which are actual device patterns with relatively large critical-dimension and image errors with respect to on-wafer targets. Hotspots can be formed under a variety of conditions such as the original design being unfriendly to the resolution enhancement technique that is applied, unanticipated pattern combinations in rule-based optical proximity correction (OPC), or inaccuracies in model-based OPC. When these hotspots fall on locations that are critical to the electrical performance of a device, device performance and parametric yield can be significantly degraded. The golden verification signoff tool using a simulation-based approach has occupied the mainstream and has been able to accurately detect hotspots. However, this approach represents a runtime-quality tradeoff point that is high in quality but also high in runtime. There is also little point in trying to replace the golden signoff tool. We are motivated to develop a low-runtime "prefilter" that reduces the amount of layout area to be analyzed by the golden tool, without compromising the overall quality of hotspot finding. In this paper, we first describe a novel detection algorithm for hotspots induced by lithographic uncertainty. Our goal is to rapidly detect all lithographic hotspots without significant accuracy degradation. In other words, we propose a filtering method: as long as,there are no "false negatives," i.e., we reliably obtain a superset of actual hotspots, then our method can dramatically reduce the layout area processed by golden hotspot analysis. Our hotspot detection algorithm includes layout graph construction, graph planarization, three-level bridging hotspot detection, and necking hotspot detection. We have tested our flow on several industry test cases. The experimental results show that our method is promising: for benchmark designs in 90-nm and 65-nm technologies, 100% of bridging and open hotspots are detected with few falsely detected hotspots. The average runtime of our method is more than 496× faster compared to the commercial tool. [ABSTRACT FROM AUTHOR]

Subresolution assist feature (SRAF) and etch-dummy insertion techniques have been absolutely essential for process window enhancement and CD control in photo and etch processes. However, as focus levels change during lithography manufacturing, CDs at a given "legal" pitch can fail to achieve manufacturing tolerances. Placed standard-cell layouts may not have the ideal whitespace distribution to allow for an optimal assist-feature insertion. This paper first describes a novel dynamic programming-based technique for Assist-Feature Correctness (AFCorr) in detailed placement of standard-cell designs. At the same time, etch-dummy features are used in the mask data preparation flow to reduce CD skew between resist and etch processes and to improve the printability of layouts. However, etch-dummy rules conflict with the SRAF insertion because each of the two techniques requires specific design rules. We further present a novel SRAF-aware etch-dummy insertion method (SAEDM) which optimizes the etch-dummy insertion to make the layout more conducive to the assist-feature insertion after the etch-dummy features have been inserted. Since placement of cells can create forbidden-pitch violations of resist process and can increase etch skew, the placer must also generate etch- dummy-correct placement. This can be solved by Etch-dummy Correctness (EtchCorr), which is an intelligent whitespace management for etch-dummy-corrected placement, an extension of the AFCorr methodology. These methods for enhanced resist and etch CD control are validated on industrial test cases with respect to wafer printability, database complexity, and device performance. For benchmark designs, we validate the four methodologies: 1) AFCorr; 2) SAEDM; 3) AFCorr + SAEDM; and 4) AFCorr + EtchCorr + SAEDM. The AFCorr placement perturbation achieves a significant reduction in forbidden pitches between polysilicon shapes. Using 1) flow, forbidden-pitch count of photo process is reduced by 76%-100% for 130 nm and by 87%-100% for 90 nm. Our novel Corr design-perturbation technique, which combines the AFCorr and EtchCorr methods, facilitates additional SRAF and etch-dummy insertions and, thus, reduces the CD skew between the photo and etch processes. After Corr with SAEDM, edge placement error (EPE) count is also reduced by 91%-100% in the resist CD and by 72%-98% in the etch CD. Our methods provide a substantial improvement in CD control with negligible timing, area, and CPU overhead. The advantages of such correctness methods are expected to increase in future technology nodes. [ABSTRACT FROM AUTHOR]

In order to develop security systems for identity authentication, face recognition (FR) technology has been applied. One of the main problems of applying FR technology is that the systems are especially vulnerable to attacks with spoofing faces (e.g., 2D pictures). To defend from these attacks and to enhance the reliability of FR systems, many anti-spoofing approaches have been recently developed. In this paper, we propose a method for face liveness detection using the effect of defocus. From two images sequentially taken at different focuses, three features, focus, power histogram and gradient location and orientation histogram (GLOH), are extracted. Afterwards, we detect forged faces through the feature-level fusion approach. For reliable performance verification, we develop two databases with a handheld digital camera and a webcam. The proposed method achieves a 3.29% half total error rate (HTER) at a given depth of field (DoF) and can be extended to camera-equipped devices, like smartphones. [ABSTRACT FROM AUTHOR]

Optical centroid efficiency (OCE) is the traditional enclosed energy by the square detector pixel (the energy on detector (EOD)) averaged over all misalignments between the optical system and the pixel center. We examine the relationship between the OCE and the EOD for two different pixel sizes, with one larger than the diameter of the Airy disc and the other smaller than three diameters of the Airy disc. We observed that the relationships could, in general, be described as having a U shape, with the OCE being proportional to the EOD for small amounts of aberrations and inversely proportional for large amounts. For the letter cases, the energy is distributed over several pixels. [ABSTRACT FROM AUTHOR]

In tunnel boring machine construction sites, on-site calibration of large field-of-view binocular cameras is still a challenging task due to factors such as the narrow spaces of the machines' mechanical structures, high pressure, or falling rocks. Therefore, a calibration method using a small plate in proximity to the camera (a defocused position safe for workers) is proposed in this paper to deal with the above challenges. First, a defocus-insensitive feature point encoding pattern is developed. Then feature points are extracted through a phase recovery network based on the Swin Transformer, and finally camera parameters are optimized based on virtual symmetric transfer errors. The relevant simulation and experimental data show that the relative error between the proposed method and the phase-shift method in feature extraction is 0.15 pixels, while the relative error between the proposed method and a high-precision laser tracker in three-dimensional measurement is 1.3%. The results demonstrate the feasibility of the method in a large field-of-view binocular camera for tunnel boring machine construction sites. Increasing the diversity of data sets and lightweight models will be the focus of future research. • Feature point encoding using a defocus-insensitive global coordinate encoder. • Proposed PRT network to recover the phase information of feature points. • Optimization of camera parameters using a virtual symmetric transfer error. [ABSTRACT FROM AUTHOR]

In this paper, attempts have been made to study the joint effects of apodization and aperture masking on the diffraction images of coherently illuminated straight edge. The Edge-Ringing, Edge-Shift and Edge-Eradient of the edge images have been evaluated for different values of apo-dization using edge masking of circular apertures. We have considered rotationally symmetric, ab-errated coherent optical system. These investigations have lead to the use of certain pupil functions in conjunction with optimal apodizers to assess the quality of edge images. Any obstruction placed in the light path of an optical system prevents waves from a portion of the wavefront in reaching the focal zone. This results in the change in the light flux at every point of the diffraction pattern. This is in turn, depends on the shape and size of the obstruction.

The multi-slice integration (MSI) method is one of the approachs to extend the depth of view (DOV) of the pulsed laser range-gated imaging (PLRGI) system. When the DOV is large enough and exceeds the depth of focus of the system, it may make some targets in the image clear and others blurred. In addition, forward scatter is also considered to have a blurring effect on the image. There is very little literature to solve the combined effect of forward scatter and defocus. An imaging model is built based on the model from Jaffe–McGlamery and Fourier optics. According to the imaging model, backscattered light is independent from reflected light from the target, and forward scatter has a relationship with the reflected light. Thus, backscattered light should be removed before deblurring. First, rolling ball and intensity transformation are used to remove the backscattered light and enhance the image. Then, a deep learning model based on Transformer is used to deblur the image. To enable the deep learning model to accommodate different degrees of blurred image, 16 different blur kernels are generated according to the imaging model. Sharp images from a DPDD dataset were chosen to train the model. Images of varying degrees of blur were collected from a water tank and a boat tank by the PLRGI system as test sets. Image deblurring results show that the proposed method can remove different levels of blur and can deal with images which have sharp targets and blurred targets together. [ABSTRACT FROM AUTHOR]

Classical image segmentation techniques in computer vision exploit visual cues such as image edges, lines, color and texture. Due to the complexity of real scenarios, the main challenge is achieving meaningful segmentation of the imaged scene since real objects have substantial discontinuities in these visual cues. In this paper, a new focus-based perceptual cue is introduced: the focus signal . The focus signal captures the variations of the focus level of every image pixel as a function of time and is directly related to the geometry of the scene. In a practical application, a sequence of images corresponding to an autofocus sequence is processed in order to infer geometric information of the imaged scene using the focus signal. This information is integrated with the segmentation obtained using classical cues, such as color and texture, in order to yield an improved scene segmentation. Experiments have been performed using different off-the-shelf cameras including a webcam, a compact digital photography camera and a surveillance camera. Obtained results using Dice’s similarity coefficient and the pixel labeling error show that a significant improvement in the final segmentation can be achieved by incorporating the information obtained from the focus signal in the segmentation process. [ABSTRACT FROM AUTHOR]

In the field of 3D measurement, digital fringe projection (DFP) techniques have become a hotspot of research and application due to its non-contact, low cost and high resolution characteristics, but the gamma effect caused by projector seriously affects the 3D measurement quality of DFP techniques. This paper presents a Tabu Search optimization algorithm to generate high-quality binary patterns, which are projected and defocused to obtain the wrap phase, which can avoid the influence of gamma effect on phase quality. Secondly, in order to solve the problem that it is difficult to maintain the degree of defocus, a defocus adaptive optimization system is built. Finally, in order to reduce the influence of projector defocus on Gray code phase unwrapping, a combined Gray code method is proposed, which can accurately unwrap the wrap phase and reduce the interference of the color of object on phase unwrapping. The experimental results show that the proposed method improves the 3D measurement results of DFP techniques and improves the robustness.

Defocus is an important factor that causes image quality degradation of optoelectronic tracking equipment in the shooting range. In this paper, an improved blind/referenceless image spatial quality evaluator (BRISQUE) algorithm is formulated by using the image characteristic extraction technology to obtain a characteristic vector (CV). The CV consists of 36 characteristic values that can effectively reflect the defocusing condition of the corresponding image. The image is evaluated and scored subjectively by the human eyes. The subjective evaluation scores and CVs constitute a set of training data samples for the defocusing evaluation model. An image database that contains sufficiently many training samples is constructed. The training model is trained to obtain the support vector machine (SVM) model by using the regression function of the SVM. In the experiments, the BRISQUE algorithm is used to obtain the image feature vector. The method of establishing the image definition evaluation model via SVM is feasible and yields higher subjective and objective consistency.

Plasma sheath poses a serious challenge to inverse synthetic aperture radar (ISAR) imaging of hypersonic targets. This paper investigated the distribution characteristics of the electron density and velocity field in the plasma sheath surrounding the hypersonic target in various flight scenes. The incident depth and reflective surface of electromagnetic (EM) waves with X-band, Ku-band, and Ka-band can be determined based on the plasma frequency. We established the echo model coupled with the velocity field of the plasma sheath on the reflective surface and obtained one-dimensional range profiles and ISAR images of the hypersonic target in various flight scenes. The simulation results indicated that the non-uniform velocity field on the reflective surface induced displacement and diffusion in the one-dimensional range profile, resulting in ISAR image distortion. A changing flight scene and radar frequency can have an impact on imaging results. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) were utilized to assess the impact of plasma sheath on ISAR images. This study revealed the defocus mechanism of the ISAR image caused by the velocity field of the plasma sheath and provided a theoretical reference for the selection of radar frequency for hypersonic targets in various flight scenes.

A variety of factors that change the topography of the cornea may also induce changes in peripheral refraction. Purpose. The paper is aimed at assessing the peripheral refraction and retinal contour of myopic eyes after FS-LASIK and orthokeratological (Ortho-k) correction. Materials and methods. We examined a total of 30 patients (60 eyes) aging from 28.86 ±2.83 years which included 12 patients (24 eyes) with myopia of -5.11 ± 0.5 D and with an axial length (AL) of 25.04 ±0.33 mm before and 1 month after FS-LASIK surgery, and also included 18 patients (36 eyes) with myopia of -5.4 ± 0.24 D and AL of 25.78 ± 0.2 mm who wore ESA-DL Ortho-k lenses. The peripheral refraction of all the patients was measured using the Grand Seiko Open-field binocular autoref/keratometer and the peripheral eye length was measured using the IOL Master 500 (Carl Zeiss) at 15° and 30º nasally (N) and temporally (T), respectively, from the center of fovea. Results. The peripheral eye length measured before and after FS-LASIK as well as after Ortho-k correction was less in all peripheral zones than in the center, which corresponds to characteristics observed in hyperopic peripheral defocus. Refraction measured after FS-LASIK showed the formation of myopic defocus with a maximum at 30° from the following results: -2.49 D at T15°, -2.5 D at N15°, -6.73 D at T30°, and -7.8 D at N30°. The maximal myopic defocus after Ortho-k correction was detected in the middle periphery from these following results: -4.89 D at T15°, -5.51 D at N15°, -2.92 D at T30° and -2.4 D at N30°. Conclusions. Both procedures induced a significant peripheral myopic defocus. In the first case, the maximum values of defocus were detected in the peripheral zone (30° from the center of the fovea); in the second case, the maximal effect on the middle periphery was identified 15° from the center. Such patterns of peripheral refraction fully coincided with the specific changes in corneal topography after the two procedures. The retinal contour within 30° from the center retained the relative hyperopic defocus characteristic of intact myopic eyes // Russian Ophthalmological Journal, 2017; 1: 31-5. doi: 10.21516/2072-0076-2017-10-1-31-35.

We present the focal flow sensor. It is an unactuated, monocular camera that simultaneously exploits defocus and differential motion to measure a depth map and a 3D scene velocity field. It does this using an optical-flow-like, per-pixel linear constraint that relates image derivatives to depth and velocity. We derive this constraint, prove its invariance to scene texture, and prove that it is exactly satisfied only when the sensor’s blur kernels are Gaussian. We analyze the inherent sensitivity of the focal flow cue, and we build and test a prototype. Experiments produce useful depth and velocity information for a broader set of aperture configurations, including a simple lens with a pillbox aperture. [ABSTRACT FROM AUTHOR]

We propose in this work a model for describing visual acuity (V) as a function of defocus and pupil diameter. Although the model is mainly based on geometrical optics, it also incorporates nongeometrical effects phenomenologically. Compared to similar visual acuity models, the proposed one considers the effect of astigmatism and the variability of best corrected V among individuals; it also takes into account the accommodation and the "tolerance to defocus," the latter through a phenomenological parameter. We have fitted the model to the V data provided in the works of Holladay et al. and Peters, showing the ability of this model to accurately describe the variation of V against blur and pupil diameter. We have also performed a comparison between the proposed model and others previously published in the literature. The model is mainly intended for use in the design of ophthalmic compensations, but it can also be useful in other fields such as visual ergonomics, design of visual tests, and optical instrumentation. [ABSTRACT FROM AUTHOR]

We present confocal stereo, a new method for computing 3D shape by controlling the focus and aperture of a lens. The method is specifically designed for reconstructing scenes with high geometric complexity or fine-scale texture. To achieve this, we introduce the confocal constancy property, which states that as the lens aperture varies, the pixel intensity of a visible in-focus scene point will vary in a scene- independent way, that can be predicted by prior radiometric lens calibration. The only requirement is that incoming radiance within the cone subtended by the largest aperture is nearly constant. First, we develop a detailed lens model that factors out the distortions in high resolution SLR cameras (12MP or more) with large-aperture lenses (e.g., f1.2). This allows us to assemble an A× F aperture-focus image (AFI) for each pixel, that collects the undistorted measurements over all A apertures and F focus settings. In the AFI representation, confocal constancy reduces to color comparisons within regions of the AFI, and leads to focus metrics that can be evaluated separately for each pixel. We propose two such metrics and present initial reconstruction results for complex scenes, as well as for a scene with known ground-truth shape. [ABSTRACT FROM AUTHOR]

We propose a method for estimating depth from images captured with a real aperture camera by fusing defocus and stereo cues. The idea is to use stereo-based constraints in conjunction with defocusing to obtain improved estimates of depth over those of stereo or defocus alone. The depth map as well as the original image of the scene are modeled as Markov random fields with a smoothness prior, and their estimates are obtained by minimizing a suitable energy function using simulated annealing. The main advantage of the proposed method, despite being computationally less efficient than the standard stereo or DFD method, is simultaneous recovery of depth as well as space-variant restoration of the original focused image of the scene. [ABSTRACT FROM AUTHOR]

Depth from Focus (DFF) and Depth from Defocus (DFD) methods are theoretically unified with the geometric triangulation principle. Fundamentally, the depth sensitivities of DFF and DFD are not different than those of stereo (or motion) based systems having the same physical dimensions. Contrary to common belief, DFD does not inherently avoid the matching (correspondence) problem. Basically, DFD and DFF do not avoid the occlusion problem any more than triangulation techniques, but they are more stable in the presence of such disruptions. The fundamental advantage of DFF and DFD methods is the two-dimensionality of the aperture, allowing more robust estimation. We analyze the effect of noise in different spatial frequencies, and derive the optimal changes of the focus settings in DFD. These results elucidate the limitations of methods based on depth of field and provide a foundation for fair performance comparison between DFF/DFD and shape from stereo (or motion) algorithms. [ABSTRACT FROM AUTHOR]

Light field technologies have seen a rise in recent years and microscopy is a field where such technology has had a deep impact. The possibility to provide spatial and angular information at the same time and in a single shot brings several advantages and allows for new applications. A common goal in these applications is the calculation of a depth map to reconstruct the three-dimensional geometry of the scene. Many approaches are applicable, but most of them cannot achieve high accuracy because of the nature of such images: biological samples are usually poor in features and do not exhibit sharp colors like natural scene. Due to such conditions, standard approaches result in noisy depth maps. In this work, a robust approach is proposed where accurate depth maps can be produced exploiting the information recorded in the light field, in particular, images produced with Fourier integral Microscope. The proposed approach can be divided into three main parts. Initially, it creates two cost volumes using different focal cues, namely correspondences and defocus. Secondly, it applies filtering methods that exploit multi-scale and super-pixels cost aggregation to reduce noise and enhance the accuracy. Finally, it merges the two cost volumes and extracts a depth map through multi-label optimization. [ABSTRACT FROM AUTHOR]