Light Distortion Research Articles

The Velocity Aberration Effect of the CSST Main Survey Camera* *This research has been generously supported by the National Natural Science Foundation of China (NSFC, Grant Nos. 12073047 and 12273077) and the National Key Research and Development (Grant No. 2022YFF0711500).

In this study, we conducted simulations to find the geometric aberrations expected for images taken by the Main Survey Camera of the Chinese Space Station Telescope (CSST) due to its motion. As anticipated by previous work, our findings indicate that the geometric distortion of light impacts the focal plane's apparent scale, with a more pronounced influence as the size of the focal plane increases. Our models suggest that the effect consistently influences the pixel scale in both the vertical and parallel directions. The apparent scale variation follows a sinusoidal distribution throughout one orbital period. Simulations reveal that the effect is particularly pronounced in the center of the Galaxy and gradually diminishes along the direction of ecliptic latitude. At low ecliptic latitudes, the total aberration leads to about a 0.94 pixel offset (a 20 minute exposure) and a 0.26 pixel offset (a 300 s exposure) at the edge of the field of view. Appropriate processings for the geometric effect during the CSST pre- and post-observation phases are presented.

GFRENet: An Efficient Network for Underwater Image Enhancement with Gated Linear Units and Fast Fourier Convolution

Underwater image enhancement is critical for a variety of marine applications such as exploration, navigation, and biological research. However, underwater images often suffer from quality degradation due to factors such as light absorption, scattering, and color distortion. Although current deep learning methods have achieved better performance, it is difficult to balance the enhancement performance and computational efficiency in practical applications, and some methods tend to cause performance degradation on high-resolution large-size input images. To alleviate the above points, this paper proposes an efficient network GFRENet for underwater image enhancement utilizing gated linear units (GLUs) and fast Fourier convolution (FFC). GLUs help to selectively retain the most relevant features, thus improving the overall enhancement performance. FFC enables efficient and robust frequency domain processing to effectively address the unique challenges posed by the underwater environment. Extensive experiments on benchmark datasets show that our approach significantly outperforms existing state-of-the-art techniques in both qualitative and quantitative metrics. The proposed network provides a promising solution for real-time underwater image enhancement, making it suitable for practical deployment in various underwater applications.

Underwater Imagery Enhancement with Multi-Channel Histogram Equalization, Depth-Adaptive Correction, and Deep Reinforcement Learning

Underwater imagery plays a pivotal role in various scientific, industrial, and recreational applications, ranging from marine biology and oceanography to underwater archaeology and resource exploration. However, capturing high-quality images in underwater environments poses unique challenges due to factors such as light attenuation, color distortion, and particulate matter suspended in the water. In recent years, significant advancements have been made in the development of advanced techniques for enhancing underwater imagery aimed at improving perceptual quality, sharpness, and detail preservation. This research explores state-of-the-art enhancement techniques, focusing on multi-channel histogram equalization and depth-adaptive correction in the context of deep reinforcement learning (DRL) and variational autoencoders (VAE). The research presents a comprehensive analysis of the performance of these techniques using numerical metrics such as perceptual quality, sharpness, and detail preservation, derived from experimental evaluations. The findings demonstrate the effectiveness of VAE in enhancing perceptual quality and sharpness, with underwater image dehazing exhibiting commendable results as well. Furthermore, Convolutional Neural Networks (CNN) emerge as a promising approach for detail preservation in underwater images. By advancing the capabilities of underwater image enhancement techniques, this research contributes to the broader goal of unlocking the full potential of underwater exploration and understanding. The findings reveal significant improvements in image quality achieved by these techniques. VAE exhibits the highest perceptual quality scores, averaging around 0.84, with sharpness scores reaching approximately 0.91.

Alternative Media Vary Between Mild Distortion and Extreme Misinformation: Steps Toward a Typology

Social media has faced criticism for promoting misinformation. The role of alternative media in spreading misinformation, however, remains uncertain. We examined 1661 Facebook posts from 25 most popular alternative media outlets during the Covid-19 pandemic in five countries (France, Germany, Switzerland, the UK, and the US). Our codebook covered a wide range of categories, from mild misleading content to blatant misinformation. Through cluster analysis, we identified four reporting types in alternative media: Light distortion, heavy distortion, ideological misinformation, and extreme misinformation. Light and heavy distortion were most prevalent in popular alternative media, while ideological and extreme misinformation had smaller but more engaged audiences. In summary, alternative media found more success on Facebook with content categorized as light and heavy distortion. This typology recognizes complexity in misinformation and alternative media research, while simultaneously reduces complexity to contribute to comparative studies in the future.

Design of Abrasion-Resistant, Long-Lasting Antifog Coatings.

Fog formation is a common challenge for numerous applications, such as food packaging, mirrors, building windows, and freezer/refrigerator doors. Most notably, fog forms on the inner surfaces of prescription glasses and safety eyewear (particularly when used with a mask), face shields, and helmet lenses. Fogging is caused by the distortion of light from condensed water droplets present on a surface and can typically be prevented if the surface static water contact angle (θ) is less than ∼40°. Such a low contact angle can be readily achieved by either increasing the substrate surface energy or by engineering surface nanotexture. Unfortunately, such nanotexture can be readily damaged with use, while high surface energy substrates get covered with low surface energy foulants over time. Consequently, even with numerous ephemeral antifog coatings, currently there are no commercially available, durable, and permanent antifog coatings. Here we discuss the development of a new class of high-performance antifog coatings that are abrasion-resistant and long-lasting. These polyvinylpyrrolidone-based coatings, designed based on the classical Ratner-Lancaster wear model, dramatically outperform the base polymer, as well as all tested commercially available antifog coatings. Specifically, these coatings exhibit a > 400% increase in fogging time compared to base polymer, a > 50,000% increase in wear resistance, and excellent long-term antifog performance. The developed coatings also significantly outperformed all tested commercially available antifog coatings in terms of their antifog performance, wear resistance, and long-term cyclical performance. Additionally, the key design strategies employed here─incorporation of toughening agents and hydrophilic slip additives─offer a new approach to developing high-performance, durable antifog coatings based on other well-known antifog polymers.

Underwater Image Restoration and Object Detection

Underwater environments present unique challenges for imaging due to factors such as light attenuation, scattering, and colour distortion. This research combines advanced CNN models like CBAM(convolutional Block Attention Mod-ule) and VGG16 with state-of-the-art object detection methods of CNN like YOLO or RCNN to enhance the visual quality of underwater images and to detect the objects based on an accuracy rate. Leveraging the various capabilities of the VGG16 model, pretrained on extensive datasets, the system efficiently restores degraded underwater images by capturing and learning intricate features. Integrating the CBAM model enhances this process by selectively attending to salient features while suppressing irrelevant ones, thereby refining the restoration results. Additionally, the combined architecture facilitates object detection within the restored images, enabling the identification and localization of submerged objects with high accuracy. Currently the work presents short review on the existing methods of underwater image restoration and a suggests method employing the CBAM(convolutional Block Attention Mod-ule) and VGG16 to overcome the prevailing challenges in underwater object detection. In future, the research aims to present a website that would be more useful for the students , researchers and the underwater explorers.

HCLR-Net: Hybrid Contrastive Learning Regularization with Locally Randomized Perturbation for Underwater Image Enhancement

AbstractUnderwater image enhancement presents a significant challenge due to the complex and diverse underwater environments that result in severe degradation phenomena such as light absorption, scattering, and color distortion. More importantly, obtaining paired training data for these scenarios is a challenging task, which further hinders the generalization performance of enhancement models. To address these issues, we propose a novel approach, the Hybrid Contrastive Learning Regularization (HCLR-Net). Our method is built upon a distinctive hybrid contrastive learning regularization strategy that incorporates a unique methodology for constructing negative samples. This approach enables the network to develop a more robust sample distribution. Notably, we utilize non-paired data for both positive and negative samples, with negative samples are innovatively reconstructed using local patch perturbations. This strategy overcomes the constraints of relying solely on paired data, boosting the model’s potential for generalization. The HCLR-Net also incorporates an Adaptive Hybrid Attention module and a Detail Repair Branch for effective feature extraction and texture detail restoration, respectively. Comprehensive experiments demonstrate the superiority of our method, which shows substantial improvements over several state-of-the-art methods in terms of quantitative metrics, significantly enhances the visual quality of underwater images, establishing its innovative and practical applicability. Our code is available at: https://github.com/zhoujingchun03/HCLR-Net.

Screening of positive dysphotopsia before multifocal intraocular lens implantation.

To assess the photic phenomena (PP) and positive dysphotopsia in candidates for presbyopia or cataract surgery and to evaluate their relationship with cataract grading systems. Retrospective observational. Monocular data for 82 subjects measured during the preoperative screening were retrospectively retrieved from our database. The evaluated variables consisted of two methods for PP measurement: light distortion index (LDI) and parameters obtained from a simulator, both of which were combined with subjective bother related to PP. The cutoff for LDI that better predicted patients passing from slightly to moderately bothersome was estimated. The relationships between LDI and the following objective cataract grading methods were also assessed: objective scatter index (OSI), dysfunctional lens index (DLI), and Pentacam Nucleus Staging (PNS). LDI was the best method for measuring PP, which showed a significant correlation with the bothersome question (rho = 0.34, P = 0.002) and also with OSI (rho = 0.67, P < 0.0005), DLI (rho = -0.29, P = 0.007), and PNS (rho = 0.48, P < 0.0005). The number/percentage of patients who found it bothersome was as follows: "Not at all" (18/22%), "Slightly" (41/50%), "Moderately" (15/18.3%), and "Very" (8/9.7%). The cutoff value that predicted the transition from slightly to moderately bothersome was ≥15.20% according to LDI, which could be estimated with the following values for grading: ≥2.8 for OSI, ≤7.6 for DLI, and ≥2 for PNS. Patients reporting moderately or higher bothersome levels in the preoperative period and with LDI <15.20%, <2.8 for OSI, >7.6 for DLI, and <2 for PNS might deserve special attention in the multifocal intraocular lens selection.

Adaptive optics ‐ Basic optical principles

Adaptive Optics (AO) refers to a technique to compensate for distortions caused by optical aberrations in the media between the camera and the object being imaged. It was originally developed for use in astronomical telescopes to compensate for optical distortions induced by the inhomogeneous earth atmosphere. It has since evolved to become a powerful clinical tool in ophthalmology. In the eye, a “wavefront sensor” (aberrometer) measures the distortion of incoming light induced by inhomogeneities within the cornea and crystalline lens, which is then “undistorted” via reflection by a deformable mirror. AO thus enables imaging of the human retina with unprecedented resolution in vivo, such as revealing individual photoreceptors or the walls of blood vessels. One should note that AO by itself does not provide an image; rather an AO subsystem is incorporated into an existing imaging device. AO subsystems have thus far been successfully integrated into three ophthalmic imaging devices: fundus cameras, scanning laser ophthalmoscopes, and the OCT device. This lecture will introduce the basic principles of AO, illustrate its value with state‐of‐the‐art clinical examples, and discuss potential future applications in ophthalmology.

Synthetic aperture optical image restoration based on multi-scale feature enhancement

With the wide applications of high-resolution imaging technology in topographic mapping, astronomical observation, and military reconnaissance and other fields, the requirements for imaging resolution of optical system are becoming higher and higher . According to the diffraction limit and Rayleigh criterion, the imaging resolution of the optical system is proportional to the size of the aperture of the system, but affected by the material and the processing of the optical component: the single aperture of the optical system cannot be infinitely enlarged. Therefore the synthetic aperture technology is proposed to replace the single large aperture optical system. Owing to the effect of sub-aperture arrangement and light scattering, the imaging of synthetic aperture optical system will be degraded because of insufficient light area and phase distortion. The traditional imaging restoration algorithm of synthetic aperture optical system is sensitive to noise, overly relies on degraded model, requires a lot of manually designed models, and has poor adaptability. To solve this problem, a multi-scale feature enhancement method of restoring the synthetic aperture optical image is proposed in this work. U-Net is used to obtain multi-scale feature, and self-attention in mixed domain is used to improve the ability of of the network to extract the features in space and channel. Multi-scale feature fusion module and feature enhancement module are constructed to fuse the information between features on different scales. The information interaction mode of the codec layer is optimized, the attention of the whole network to the real structure of the original image is enhanced, and the artifact interference caused by ringing is avoided in the process of restoration. The final experimental results are 1.51%, 4.42% and 5.22% higher than those from the advanced deep learning algorithms in the evaluation indexes of peak signal-to-noise ratio, structural similarity and perceived similarity, respectively. In addition, the method presented in this work has a good restoration effect on the degraded images to different degrees of synthetic aperture, and can effectively restore the degraded images and the images with abnormal light, so as to solve the problem of imaging degradation of synthetic aperture optical system. The feasibility of deep learning method in synthetic aperture optical image restoration is proved.

Underwater Image Restoration System

Abstract: Underwater imaging poses significant challenges due to light absorption, scattering, and color distortion. This paper introduces an innovative underwater image restoration system designed to enhance the visibility and analytical capabilities of underwater imaging. The proposed system employs advanced image processing techniques to address the inherent issues associated with underwater photography. The methodology involves the development of a model that accounts for the optical properties of water, including attenuation and scattering. By leveraging this model, the system corrects color distortions and enhances contrast, leading to improved clarity in underwater images. Additionally, a novel algorithm is employed to reduce the impact of particulate matter, such as suspended sediments, contributing to a clearer representation of the underwater scene. The system incorporates machine learning approaches for adaptive filtering, allowing it to dynamically adjust parameters based on environmental conditions. This adaptability enables the restoration system to perform effectively across a range of underwater scenarios, from clear to turbid waters.

Improved GCC Technique: A Comprehensive Approach to Color Cast Rectification and Image Enhancement

The domain of underwater imaging is riddled with multifarious challenges, such as light attenuation, scattering, and color distortion, which can have a detrimental impact on the quality of images. In order to address these challenges, the Generalized Color Compensation (GCC) technique has been introduced, which utilizes color compensation and color mean adjustment to rectify color cast while integrating contrast enhancement via the Contrast Limited Adaptive Histogram Equalization (CLAHE). Nevertheless, the performance of GCC is limited due to the production of bright and smooth images. To overcome this challenge, we have introduced the improved GCC approach, which employs color compensation and color mean adjustment to rectify color cast. Subsequently, a contrast-enhanced image is generated through CLAHE to improve image contrast, while the detail-enhanced image is produced via a cumulative distribution function. Furthermore, image fusion between the detail-enhanced and contrast-enhanced images yields a superior-quality image. Our experimental results demonstrate the effectiveness of our proposed technique in improving the visual quality of underwater images. Objective metrics such as Underwater Image Quality Measure (UIQM) demonstrate that our technique surpasses GCC in terms of image sharpness, colorfulness, and contrast.

Standard Clinical Outcomes, Light Distortion, Stereopsis, and a Quality-of-Life Assessment of a New Binocular System of Complementary IOLs.

To evaluate the visual outcome, light distortion index (LDI), and quality of life (QoL) of patients implanted with two complementary intraocular lenses (IOLs) to treat cataract and presbyopia. Twenty-seven consecutive patients with cataract were treated with the implantation of the Artis Symbiose Mid (Mid) IOL (Cristalens Industrie) in the distance-dominant eye and the Artis Symbiose Plus (Plus) IOL (Cristalens Industrie) in the contralateral eye following phacoemulsification. The primary objective was to ascertain the monocular and binocular defocus curves. Secondary endpoints included uncorrected distance visual acuity, corrected distance visual acuity, uncorrected intermediate visual acuity, and distance-corrected intermediate visual acuity at 90 and 70 cm, uncorrected near visual acuity and distance-corrected visual acuity at 40 cm, contrast sensitivity, LDI with a halometer, stereopsis, and patients' QoL with the validated Visual Function Index (VF-14) questionnaire. These measurements were collected in two visits, at 4.14 ± 3.13 and 10.30 ± 3.14 months postoperatively. Statistically significant differences in the monocular defocus curves were found at the defocus steps of -1.00, -1.25, -1.50, -1.75, -2.50, -2.75, -3.00, -3.50 diopters and the -4.00 diopters (P < .050). The mean binocular defocus curve was 0 logMAR or better from the +0.50 to the -2.50 D defocus steps. Contrast sensitivity was within normal values. The LDI was 12.57 (6.61)% for the Mid eyes, 14.99 ± 5.70% for the Plus eyes, and 10.36 ± 4.42% binocularly. The patients' stereopsis was 40.0 (12.5) arc-seconds. The QoL score was 95.99 (7.14) at 10 months. The implantation of the Artis Symbiose IOLs was a safe and effective treatment for presbyopia compensation in patients with cataract. Both IOLs are complementary and may produce a binocular depth-of-field of 3.00 diopters over 0 logMAR when used together. [J Refract Surg. 2023;39(10):654-661.].

The invariance and distortion of vectorial light across a real-world free space link

Vectorial structured light, where the polarization is inhomogeneously distributed in space, has found a myriad of applications in both 2D and 3D optical fields. Here, we present an experimental study of the invariance and distortion of vectorial light through a real-world medium of atmospheric turbulence. We show that the amplitude and polarization structure are both severely distorted by the turbulent medium, yet the non-separability of these two degrees of freedom remains invariant. We monitor this invariance under a range of beam types and atmospheric conditions, over extended time periods, revealing the unitary nature of atmospheric turbulence in our experiment. Our results provide conclusive evidence that invariance and distortion are not mutually exclusive and that the degree of classical entanglement remains unaltered through such channels, and will be of interest to the large community interested in classical and quantum communication in free space.

Influence of a multifocal intraocular lens centration and eye angles on light distortion and ocular scatter index.

To assess how eye axes and multifocal intraocular lens (MIOL) centration may impact the light distortion index (LDI) and ocular scatter index (OSI). Fifty-eight subjects implanted with the trifocal MIOL Q-Flex M 640PM or Liberty 677MY (Medicontur) were included in this retrospective analysis. The following variables were collected with the Pentacam Wave (Oculus) considering the vertex normal as the coordinates center: chord-mu to the center of the pupil, chord-alpha to the geometrical center of the cornea, and chord-MIOL to the center of the diffractive ring. These measurements were correlated with OSI (HD Analyzer, Visiometrics) and LDI (light distortion analyzer, CEORLab). Chord-MIOL centroid was 0.12mm at 62°, chord-mu was 0.09mm at 174°, and chord-alpha was 0.38mm at 188°. A relationship was found between OSI and LDI, rho = 0.58, p < 0.0005). No relationships were found between chord-mu or chord-alpha and the LDI or OSI, neither for the total magnitude, nor the decomposition in orthogonal components (p > 0.05). The LDI was significantly correlated with the temporal centration of the MIOL versus the vertex normal (rho = 0.32, p = 0.02). As opposed to what has been previously described, the temporal centration of the MIOL was related to a decrease in the LDI. Future studies with extreme values of the included variables are required to establish cut-offs for considering these variables as exclusion criteria in the implantation of a MIOL.

Using a deep learning algorithm in image-based wavefront sensing: determining the optimum number of Zernike terms

The turbulent atmosphere usually degrades the quality of images taken on Earth. Random variations of the refractive index of light cause distortion of wavefronts propagating to ground-based telescopes. Compensating these distortions is usually accomplished by adaptive optics (AO) approaches. The control unit of AO adjusts the phase corrector, such as deformable mirrors, based on the incoming turbulent wavefront. This can be done by different algorithms. Usually, these algorithms encounter real-time wavefront compensation challenges. Although many studies have been conducted to overcome these issues, we have proposed a method, based on the convolutional neural network (CNN) as a branch of deep learning (DL) for sensor-less AO. To this objective, thousands of wavefronts, their Zernike coefficients, and corresponding intensity patterns in diverse conditions of turbulence are generated and fed into the CNN to predict the wavefront of new intensity patterns. The predictions are done for considering the different number of Zernike terms, and the optimum number is achieved by comparing wavefront errors.

Peripheral Refraction and Visual Function of Novel Perifocal Ophthalmic Lens for the Control of Myopia Progression.

This study aimed to evaluate the peripheral defocus induced with a novel perifocal ophthalmic lens for myopia progression control and the potential impact on visual function. This experimental, non-dispensing crossover study evaluated 17 myopic young adults. The peripheral refraction was measured using an open-field autorefractor, at 2.50 m from the target point, in two eccentric points, 25° temporal, 25° nasal, and central vision. Visual contrast sensitivity (VCS) was measured at 3.00 m with a Vistech system VCTS 6500 in low light conditions. Light disturbance (LD) was assessed with a light distortion analyzer 2.00 m away from the device. Peripheral refraction, VCS, and LD were assessed with a monofocal lens and perifocal lens (with an add power of +2.50 D on the temporal side of the lens, and +2.00 D on the nasal side). The results showed that the perifocal lenses induced an average myopic defocus of -0.42 ± 0.38 D (p-value < 0.001) in the nasal retina, at 25° The changes induced by the lower add power in the nasal part of the lens did not induce statistically significant changes in the refraction of the temporal retina. The VCS and LD showed no significant differences between the monofocal and perifocal lenses.

Detecting Cracks in Aerated Concrete Samples Using a Convolutional Neural Network

The creation and training of artificial neural networks with a given accuracy makes it possible to identify patterns and hidden relationships between physical and technological parameters in the production of unique building materials, predict mechanical properties, and solve the problem of detecting, classifying, and segmenting existing defects. The detection of defects of various kinds on elements of building materials at the primary stages of production can improve the quality of construction and identify the cause of particular damage. The technology for detecting cracks in building material samples is of great importance in building monitoring, in pre-venting the spread of defective material. In this paper, we consider the use of the YOLOv4 convolutional neural network for crack detection on building material samples. This was based on the creation of its own empirical database of images of samples of aerated concrete. The number of images was increased by applying our own augmentation algorithm. Optimization of the parameters of the intellectual model based on the YOLOv4 convolutional neural network was performed. Experimental results show that the YOLOv4 model developed in this article has high precision in defect detection problems: AP@50 = 85% and AP@75 = 68%. It should be noted that the model was trained on its own set of data obtained by simulating various shooting conditions, rotation angles, object deformations, and light distortions through image processing methods, which made it possible to apply the developed algorithm in practice.

Simulating motion blur and exposure time and evaluating its effect on image quality

Optimizing exposure time for low light scenarios involves a trade-off between motion blur and signal to noise ratio. A method for defining the optimum exposure time for a given function has not been described in the literature. This paper presents the design of a simulation of motion blur and exposure time from the perspective of a real-world camera. The model incorporates characteristics of real-world cameras including the light level (quanta), shot noise and lens distortion. In our simulation, an image quality target chart called the Siemens Star chart will be used, and the simulation outputs a blurred image as if captured from a camera of set exposure and set movement speed. The resulting image is then processed in Imatest in which image quality readings will be extracted from the image and consequently the relationship between exposure time, motion blur and the image quality metrics can be evaluated.

Light field camera modeling and distortion correction improvement method

Light field camera modeling and distortion correction improvement method