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Motion-onset visual evoked potentials (MO VEPs) are robust to dioptric blur when low contrast and low spatial frequency patterns are used for stimulation. To reveal mechanisms of MO VEPs robustness, we studied whether the resistance to defocus persists even when using a high-contrast checkerboard using digital defocus in the emmetropic eyes of 13 subjects (males 20-60 years). We compared the dominant components of MO VEPs to pattern-reversal VEPs (PR VEP), which are sensitive to the blur. For stimulation, we used checkerboard patterns with 15´ and 60´ checks. To defocus the checkerboard, we rendered it with a second-order Zernike polynomial ( Z 2 0 ) with an equivalent defocus of 0, 2, or 4 D. For PR VEP, the checkerboards were reversed in terms of their contrast. To evoke MO VEP, the checkerboard of 60´ checks moved for 200 ms with a speed of 5 or 10 deg/s in the cardinal directions. The MO VEP did not change in peak time (P ≥ 0.0747) or interpeak amplitude (P > 0.0772) with digital blur. In contrast, for PR VEP, the results showed a decrease in interpeak amplitude (P ≤ 6.65ˑ10-4) and an increase in peak time (P ≤ 0.0385). Thus, we demonstrated that MO VEPs evoked by checkerboard, structure containing high spatial content, can be robust to defocus.

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Imaging the internal architecture of fast-vibrating structures at micrometer scale and kilohertz frequencies poses great challenges for numerous applications, including the study of biological oscillators, mechanical testing of materials, and process engineering. Over the past decade, X-ray microtomography with retrospective gating has shown very promising advances in meeting these challenges. However, breakthroughs are still expected in acquisition and reconstruction procedures to keep improving the spatiotemporal resolution, and study the mechanics of fast-vibrating multiscale structures. Thereby, this works aims to improve this imaging technique by minimising streaking and motion blur artefacts through the optimisation of experimental parameters. For that purpose, we have coupled a numerical approach relying on tomography simulation with vibrating particles with known and ideal 3D geometry (micro-spheres or fibres) with experimental campaigns. These were carried out on soft composites, imaged in synchrotron X-ray beamlines while oscillating up to 400 Hz, thanks to a custom-developed vibromechanical device. This approach yields homogeneous angular sampling of projections and gives reliable predictions of image quality degradation due to motion blur. By overcoming several technical and scientific barriers limiting the feasibility and reproducibility of such investigations, we provide guidelines to enhance gated-CT 4D imaging for the analysis of heterogeneous, high-frequency oscillating materials.

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How does the brain achieve a seemingly veridical and 'in-focus' perception of the world, knowing how severely corrupted visual information is by the eye's optics? Optical blur degrades retinal image quality by reducing the contrast and disrupting the phase of transmitted signals. Neural adaptation can attenuate the impact of blur on image contrast, yet vision rather relies on perceptually-relevant information contained within the phase structure of natural images. Here we show that neural adaptation can compensate for the impact of optical aberrations on phase congruency. We used adaptive optics to fully control optical factors and test the impact of specific optical aberrations on the perceived phase of compound gratings. We assessed blur-induced changes in perceived phase over three distinct exposure spans. Under brief blur exposure, perceived phase shifts matched optical theory predictions. During short-term (~1h) exposure, we found a reduction in blur-induced phase shifts over time, followed by after-effects in the opposite direction-a hallmark of adaptation. Finally, patients with chronic exposure to poor optical quality showed altered phase perception when tested under fully-corrected optical quality, suggesting long-term neural compensatory adjustments to phase spectra. These findings reveal that neural adaptation to optical aberrations compensates for alterations in phase congruency, helping restore perceptual quality over time.

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Recent studies have proposed methods for extracting latent sharp frames from a single blurred image. However, these methods still suffer from limitations in restoring satisfactory images. In addition, most existing methods are limited to decomposing a blurred image into sharp frames with a fixed frame rate. To address these problems, we present an Arbitrary Time Blur Decomposition Triple Generative Adversarial Network (ABDGAN) that restores sharp frames with flexible frame rates. Our framework plays a min-max game consisting of a generator, a discriminator, and a time-code predictor. The generator serves as a time-conditional deblurring network, while the discriminator and the label predictor provide feedback to the generator on producing realistic and sharp image depending on given time code. To provide adequate feedback for the generator, we propose a critic-guided (CG) loss by collaboration of the discriminator and time-code predictor. We also propose a pairwise order-consistency (POC) loss to ensure that each pixel in a predicted image consistently corresponds to the same ground-truth frame. Extensive experiments show that our method outperforms previously reported methods in both qualitative and quantitative evaluations. Compared to the best competitor, the proposed ABDGAN improves PSNR, SSIM, and LPIPS on the GoPro test set by 16.67%, 9.16%, and 36.61%, respectively. For the B-Aist++ test set, our method shows improvements of 6.99%, 2.38%, and 17.05% in PSNR, SSIM, and LPIPS, respectively, compared to the best competitive method.

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PURPOSE: To evaluate whether colour vision normal (CVN) adults pass two Fletcher-Evans (CAM) lantern tests and to investigate the impact of imposed blur on Ishihara, CAM lantern and computerised colour discrimination test (colour assessment and diagnosis test [CAD] and Cambridge colour test [CCT]) results. METHODS: In a pilot experiment, 20 (16 CVN and 4 colour vision deficient [CVD]) participants with normal VA were tested with the CAM lantern. In the main experiment, the impact of imposed dioptric blur (up to +8.00 D) on visual acuity and the Ishihara test, CAM lantern, CAD and CCT was assessed for 15 CVN participants. RESULTS: CVN participants can fail the CAM lantern, with specificity of 81.25% (aviation mode) and 75% (clinical mode), despite following the test requirements of participants having at least 0.18 logMAR (6/9) in the better eye. With blur, test accuracy was affected. As expected, significant detrimental effects of blur on test results were found for logMAR VA and CAM lantern (aviation) with +1.00 D or higher. Ishihara, CAD and CCT results were not detrimentally affected until +8.00 D. Yellow-blue discrimination was more affected by blur for the CAD than the CCT, which was not explained by the different colour spaces used or vectors tested. CONCLUSION: False-positive findings on lantern colour vision tests with small apertures are likely to be increased in patients with blur due to uncorrected refractive error or ocular and visual pathway disease. Other colour vision tests with larger stimuli are more robust to blur.

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Despite achieving numerous successes with surface defect inspection based on deep learning, the industry still faces challenges in conducting packaging defect inspections that include critical information such as ingredient lists. In particular, while previous achievements primarily focus on defect inspection in high-quality images, they do not consider defect inspection in low-quality images such as those containing image blur. To address this issue, we proposed a noble inference technique named temporal-quality ensemble (TQE), which combines temporal and quality weights. Temporal weighting assigns weights to input images by considering the timing in relation to the observed image. Quality weight prioritizes high-quality images to ensure the inference process emphasizes clear and reliable input images. These two weights improve both the accuracy and reliability of the inference process of low-quality images. In addition, to experimentally evaluate the general applicability of TQE, we adopt widely used convolutional neural networks (CNNs) such as ResNet-34, EfficientNet, ECAEfficientNet, GoogLeNet, and ShuffleNetV2 as the backbone network. In conclusion, considering cases where at least one low-quality image is included, TQE has an F1-score approximately 17.64% to 22.41% higher than using single CNN models and about 1.86% to 2.06% higher than an average voting ensemble.

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Many species rely on celestial cues as a reliable guide for maintaining heading while navigating. In this paper, we propose a method that extracts the Milky Way (MW) shape as an orientation cue in low-light scenarios. We also tested the method on both real and synthetic images and demonstrate that the performance of the method appears to be accurate and reliable to motion blur that might be caused by rotational vibration and stabilisation artefacts. The technique presented achieves an angular accuracy between a minimum of 0.00° and a maximum 0.08° for real night sky images, and between a minimum of 0.22° and a maximum 1.61° for synthetic images. The imaging of the MW is largely unaffected by blur. We speculate that the use of the MW as an orientation cue has evolved because, unlike individual stars, it is resilient to motion blur caused by locomotion.

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OBJECTIVE: We propose a motion artifact correction algorithm (DMBL) for reducing motion artifacts in reconstructed dental cone-beam computed tomography (CBCT) images based on deep blur learning. METHODS: A blur encoder was used to extract motion-related degradation features to model the degradation process caused by motion, and the obtained motion degradation features were imported in the artifact correction module for artifact removal. The artifact correction module adopts a joint learning framework for image blur removal and image blur simulation for treatment of spatially varying and random motion patterns. Comparative experiments were conducted to verify the effectiveness of the proposed method using both simulated motion data sets and clinical data sets. RESULTS: The experimental results with the simulated dataset showed that compared with the existing methods, the PSNR of the proposed method increased by 2.88%, the SSIM increased by 0.89%, and the RMSE decreased by 10.58%. The results with the clinical dataset showed that the proposed method achieved the highest expert level with a subjective image quality score of 4.417 (in a 5-point scale), significantly higher than those of the comparison methods. CONCLUSION: The proposed DMBL algorithm with a deep blur joint learning network structure can effectively reduce motion artifacts in dental CBCT images and achieve high-quality image restoration.

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Drivers must respond promptly to a wide range of possible road hazards, from trucks veering into their lane to pedestrians stepping onto the road. While drivers' vision is tested at the point of licensure, visual function can degrade, and drivers may not notice how these changes impact their ability to notice and respond to events in the world in a timely fashion. To safely examine the potential consequences of visual degradation on hazard detection, we performed two experiments examining the impact of simulated optical blur on participants' viewing duration thresholds in a hazard detection task, as a proxy for eyes-on-road duration behind the wheel. Examining this question with older and younger participants, across two experiments, we found an overall increase in viewing duration thresholds under blurred conditions, such that younger and older adults were similarly impacted by blur. Critically, in both groups, we found that the increment in thresholds produced by blur was larger for non-vehicular road hazards (pedestrians, cyclists and animals) compared to vehicular road hazards (cars, trucks and buses). This work suggests that blur poses a particular problem for drivers detecting non-vehicular road users, a population considerably more vulnerable in a collision than vehicular road users. These results also highlight the importance of taking into account the type of hazard when considering the impacts of blur on road hazard detection.

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Background: Dry eye disease (DED) stands out as one of the most common eye conditions encountered in clinical settings. This study aimed to determine the diagnostic ability and feasibility of post-blink blur time (PBBT) in detecting patients with DED symptoms. Methods: The study included 200 subjects, 100 with and 100 without DED symptoms defined by the Schein questionnaire, who underwent assessment of DED signs [visual acuity, PBBT, conjunctival hyperemia, lid-parallel conjunctival folds-LIPCOF, tear film break-up time-TBUT, fluorescein corneal staining, and meibum score]. Results: DED subjects had a lower PBBT than controls (p < 0.001), with subjective (6 (1-45) s vs. 8 (1-70) s) and objective (6 (2-33) s vs. 8 (2-50) s) PBBT measurements being similar between repeats. The correlations between subjective and objective PBBT measurements were significantly positive (R = 0.873, p < 0.001). Subjective PBBT was negatively related to the Schein questionnaire (R = -0.217, p = 0.002), conjunctival hyperemia (R = -0.105, p = 0.035), and corneal staining (R = -0.153, p = 0.031), while positively related to the TBUT (R = 0.382, p < 0.001) and meibum score (R = 0.106, p = 0.033). Logistic regression analysis showed DED symptoms were significantly associated with subjective PBBT (AOR 0.91, p = 0.001), TBUT (AOR 0.79, p < 0.001), meibum score (AOR 0.65, p = 0.008), LIPCOF (AOR 1.18, p = 0.002) and corneal staining (AOR 1.14, p = 0.028). Conclusions: Subjective self-reported PBBT is a reliable and non-invasive screening test for evaluating time-wise changes in visual acuity and detecting a tear film dysfunction.

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Objective. Digital breast tomosynthesis (DBT) has significantly improved the diagnosis of breast cancer due to its high sensitivity and specificity in detecting breast lesions compared to two-dimensional mammography. However, one of the primary challenges in DBT is the image blur resulting from x-ray source motion, particularly in DBT systems with a source in continuous-motion mode. This motion-induced blur can degrade the spatial resolution of DBT images, potentially affecting the visibility of subtle lesions such as microcalcifications.Approach. We addressed this issue by deriving an analytical in-plane source blur kernel for DBT images based on imaging geometry and proposing a post-processing image deblurring method with a generative diffusion model as an image prior.Main results. We showed that the source blur could be approximated by a shift-invariant kernel over the DBT slice at a given height above the detector, and we validated the accuracy of our blur kernel modeling through simulation. We also demonstrated the ability of the diffusion model to generate realistic DBT images. The proposed deblurring method successfully enhanced spatial resolution when applied to DBT images reconstructed with detector blur and correlated noise modeling.Significance. Our study demonstrated the advantages of modeling the imaging system components such as source motion blur for improving DBT image quality.

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BACKGROUND: The prevalence of dry eye disease (DED) is increasing globally, resulting in a variety of eye symptoms characterized by discomfort and visual disturbances. The accurate diagnosis of the disease is often challenging and complex, requiring specialized diagnostic tools. This study aimed to investigate the impact of tear film instability on visual function and to evaluate the value of post-blink blur time (PBBT) as an alternative method for assessing tear film stability. METHODS: The study included 62 subjects: 31 with subjective symptoms of DED (Group A) and a control group consisting of 31 healthy participants (Group B). Symptoms were assessed using the standard Schein questionnaire, supplemented with additional questions. PBBT was measured using standard Snellen charts to investigate a potential association between PBBT and tear film dysfunction. Additional clinical assessments included tear film break-up time (TBUT). RESULTS: Statistically significant differences were observed in the average values of PBBT and TBUT between the examined groups. The average PBBT was 8.95 ± 5.38 s in the group with DED and 14.66 ± 10.50 s in the control group, p < 0.001. Group A exhibited an average TBUT of 4.77 ± 2.37 s, while Group B had a TBUT of 7.63 ± 3.25 s, p < 0.001. Additionally, a strong positive correlation was identified between PBBT and TBUT values (r = 0.455; p < 0.001). CONCLUSIONS: The research confirms that tear film stability has an important role in the refraction of light and the maintenance of optical quality of vision. PBBT could potentially function as an objective and clinically significant screening test for DED.

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In this article, a novel method for removing atmospheric turbulence from a sequence of turbulent images and restoring a high-quality image is presented. Turbulence is modeled using two factors: the geometric transformation of pixel locations represents the distortion, and the varying pixel brightness represents spatiotemporal varying blur. The main framework of the proposed method involves the utilization of low-rank matrix factorization, which achieves the modeling of both the geometric transformation of pixels and the spatiotemporal varying blur through an iterative process. In the proposed method, the initial step involves the selection of a subset of images using the random sample consensus method. Subsequently, estimation of the mixture of Gaussian noise parameters takes place. Following this, a window is chosen around each pixel based on the entropy of the surrounding region. Within this window, the transformation matrix is locally estimated. Lastly, by considering both the noise and the estimated geometric transformations of the selected images, an estimation of a low-rank matrix is conducted. This estimation process leads to the production of a turbulence-free image. The experimental results were obtained from both real and simulated datasets. These results demonstrated the efficacy of the proposed method in mitigating substantial geometrical distortions. Furthermore, the method showcased the ability to improve spatiotemporal varying blur and effectively restore the details present in the original image.

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In modern urban traffic systems, intersection monitoring systems are used to monitor traffic flows and track vehicles by recognizing license plates. However, intersection monitors often produce motion-blurred images because of the rapid movement of cars. If a deep learning network is used for image deblurring, the blurring of the image can be eliminated first, and then the complete vehicle information can be obtained to improve the recognition rate. To restore a dynamic blurred image to a sharp image, this paper proposes a multi-scale modified U-Net image deblurring network using dilated convolution and employs a variable scaling iterative strategy to make the scheme more adaptable to actual blurred images. Multi-scale architecture uses scale changes to learn the characteristics of different scales of images, and the use of dilated convolution can improve the advantages of the receptive field and obtain more information from features without increasing the computational cost. Experimental results are obtained using a synthetic motion-blurred image dataset and a real blurred image dataset for comparison with existing deblurring methods. The experimental results demonstrate that the image deblurring method proposed in this paper has a favorable effect on actual motion-blurred images.

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Pregnancy causes changes in all body systems, including the eye. The eye can undergo physiological and pathological changes in pregnancy. Some changes exacerbate pre-existing eye conditions while other conditions manifest for the first-time during pregnancy. Early recognition and management are essential to prevent sight threatening complications. In addition, some obstetric complications can be associated with ophthalmic signs. Prompt recognition of these eye findings may be life saving for both the mother and the foetus. The aim of this article is to present potential ocular complications in pregnancy and outline the appropriate management to preserve sight and maintain maternal and foetal safety. The safety of the use of common ophthalmological medications will also be discussed.

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Focused ion beam scanning electron microscopy (FIB-SEM) tomography is a serial sectioning technique where an FIB mills off slices from the material sample that is being analysed. After every slicing, an SEM image is taken showing the newly exposed layer of the sample. By combining all slices in a stack, a 3D image of the material is generated. However, specific artefacts caused by the imaging technique distort the images, hampering the morphological analysis of the structure. Typical quality problems in microscopy imaging are noise and lack of contrast or focus. Moreover, specific artefacts are caused by the FIB milling, namely, curtaining and charging artefacts. We propose quality indices for the evaluation of the quality of FIB-SEM data sets. The indices are validated on real and experimental data of different structures and materials.

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Myopia is the most common refractive error in the world and has reached a pandemic level. The potential complications of progressive myopia have inspired researchers to attempt to understand the sources of myopia and axial elongation and to develop modalities to arrest progression. Considerable attention has been given over the past few years to the myopia risk factor known as hyperopic peripheral blur, which is the focus of this review. It will discuss the primary theories believed to be the cause of myopia and the parameters considered to contribute to and influence the effect of peripheral blur, such as the surface retinal area of blur or the depth of blur. The multitude of optical devices designed to provide peripheral myopic defocus will be mentioned, including bifocal and progressive addition ophthalmic lenses, peripheral defocus single-vision ophthalmic lenses, orthokeratology lenses, and bifocal or multifocal center distance soft lenses, as well as their effectivity as discussed in the literature to date.

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We propose a new concept image sensor suitable for viewing and sensing applications. This is a report of a CMOS image sensor with a pixel architecture consisting of a 1.5 µm pixel with four-floating-diffusions-shared pixel structures and a 3.0 µm pixel with an in-pixel capacitor. These pixels are four small quadrate pixels and one big square pixel, also called quadrate-square pixels. They are arranged in a staggered pitch array. The 1.5 µm pixel pitch allows for a resolution high enough to recognize distant road signs. The 3 µm pixel with intra-pixel capacitance provides two types of signal outputs: a low-noise signal with high conversion efficiency and a highly saturated signal output, resulting in a high dynamic range (HDR). Two types of signals with long exposure times are read out from the vertical pixel, and four types of signals are read out from the horizontal pixel. In addition, two signals with short exposure times are read out again from the square pixel. A total of eight different signals are read out. This allows two rows to be read out simultaneously while reducing motion blur. This architecture achieves both an HDR of 106 dB and LED flicker mitigation (LFM), as well as being motion-artifact-free and motion-blur-less. As a result, moving subjects can be accurately recognized and detected with good color reproducibility in any lighting environment. This allows a single sensor to deliver the performance required for viewing and sensing applications.

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BACKGROUND: This study aimed to examine the changes to choroidal blood flow velocity using laser speckle flowgraphy (LSFG) in healthy eyes after warm water immersion at 40°C. METHODS: Data regarding the right eyes of 23 healthy volunteers were included. The mean blur rate (MBR) of the macula, which represents the choroidal blood flow velocity, was evaluated using LSFG. Intraocular pressure (IOP), systolic blood pressure (SBP), diastolic blood pressure (DBP), mean blood pressure (MBP), ocular perfusion pressure (OPP), and MBR were assessed at baseline, immediately after immersion (0 minutes), and 10, 20, and 30 minutes later. RESULTS: At 0 minutes, SBP, DBP, MBP, and OPP values were lower than those at baseline. The MBR significantly declined immediately after immersion to -6.0 ± 5.2%. However, there were no changes in these parameters after 10, 20, or 30 minutes. A significant positive correlation was observed between the MBR, SBP, DBP, MBP, and OPP values. In healthy individuals, the dominant parasympathetic activity induced by warm stimulation reduced the choroidal hemodynamic rate in the macula and decreased systemic circulatory dynamics, which normalized after 10 minutes. CONCLUSIONS: These findings suggest that the dominant parasympathetic activity induced by warm water immersion at 40°C may lead to a reduction in the systemic circulation rate and choroidal blood flow rate in the macula. These findings may help prevent and treat various retinal choroidal diseases, in which sympathetic hyperactivity is involved in the pathogenesis of the disease.

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PURPOSE: The primary aim of this research is to enhance the utilization of advanced deep learning (DL) techniques in the domain of in vitro fertilization (IVF) by presenting a more refined approach to the segmentation and organization of microscopic embryos. This study also seeks to establish a comprehensive embryo database that can be employed for future research and educational purposes. METHODS: This study introduces an advanced methodology for embryo segmentation and organization using DL. The approach comprises three primary steps: Embryo Segmentation Model, Segmented Embryo Image Organization, and Clear and Blur Image Classification. The proposed approach was rigorously evaluated on a sample of 5182 embryos extracted from 362 microscopic embryo videos. RESULTS: The study's results show that the proposed method is highly effective in accurately segmenting and organizing embryo images. This is evidenced by the high mean average precision values of 1.0 at an intersection over union threshold of 0.5 and across the range of 0.5 to 0.95, indicating a robust object detection capability that is vital in the IVF process. Segmentation of images based on various factors such as the day of development, patient, growth medium, and embryo facilitates easy comparison and identification of potential issues. Finally, appropriate threshold values for clear and blur image classification are proposed. CONCLUSION: The suggested technique represents an indispensable stage of data preparation for IVF training and education. Furthermore, this study provides a solid foundation for future research and adoption of DL in IVF, which is expected to have a significant positive impact on IVF outcomes.