Distortion Correction Research Articles

Correction of Wavefront Distortion in Common Aperture Optical Systems Based on Freeform Lens

The common aperture optical system enhances light utilization efficiency during the imaging process by utilizing a single shared aperture. This approach not only facilitates independent synchronous multi-band imaging across various applications but also reduces the complexity, size, and cost of optical systems. However, conventional common aperture optical systems typically employ inclined plates or prisms for spectral splitting, which can introduce wavefront distortion in the transmission light path, an issue that is particularly problematic in imaging systems with a large field of view. In this work, we propose employing a freeform lens to correct wavefront distortion arising from imperfections within an optical system. We present a design methodology for the freeform lens based on ray tracing techniques. The application of this freeform lens effectively mitigates wavefront distortion in an infrared dual-band composite detection system, resulting in commendable optical performance across both mid-infrared and far-infrared bands.

Quantification of the Anterior-Centripetal Movement of the Ciliary Muscle During Accommodation Using Dynamic OCT Imaging.

Although the lens undoubtedly plays a major role in presbyopia, altered lens function could be in part secondary to age-related changes of the ciliary muscle. Ciliary muscle changes with accommodation have been quantified using optical coherence tomography, but so far these studies have been limited to quantifying changes in ciliary muscle thickness, mostly at static accommodative states. Quantifying ciliary muscle thickness changes does not effectively capture the dynamic anterior-centripetal movement of the ciliary muscle during accommodation. To address this issue, we present a method to quantify the movement of the ciliary muscle during accommodation using trans-scleral optical coherence tomography images obtained dynamically. An image processing framework including distortion correction, geometric transformation, and Procrustes analysis, was used to quantify the anterior-centripetal movement of the ciliary muscle apex and centroid during accommodation. The method was applied in a preliminary study to quantify ciliary muscle displacement and its relation to lens thickness change with accommodation on two young adults and two prepresbyopes. The magnitude and the direction relative to the pupil plane of the apex/centroid displacement in response to a two diopters (2D) stimulus were 0.16/0.20 mm at 11.3°/30.5° and 0.26/0.34 mm at 6.6°/33.2° for the young adults and 0.20/0.20 mm at 29.7°/40.6° and 0.24/0.40 mm at 33.0°/31.7° for the prepresbyopes, respectively. This study demonstrates the feasibility of quantifying dynamic anterior-centripetal movement of the ciliary muscle during accommodation using optical coherence tomography. The method better captures the functional response of the muscle than the quantification of thickness changes. We provide a method that holds potential to better understand the age-related changes of the ciliary muscle on presbyopia.

An Imaging Method for Marine Targets in Corner Reflector Jamming Scenario Based on Time–Frequency Analysis and Modified Clean Technique

In the corner reflector jamming scenario, the ship target and the corner reflector array have different degrees of defocusing in the synthetic aperture radar (SAR) image due to their complex motions, which is unfavorable to the subsequent target recognition. In this manuscript, we propose an imaging method for marine targets based on time–frequency analysis with the modified Clean technique. Firstly, the motion models of the ship target and the corner reflector array are established, and the characteristics of their Doppler parameter distribution are analyzed. Then, the Chirp Rate–Quadratic Chirp Rate Distribution (CR-QCRD) algorithm is utilized to estimate the Doppler parameters. To address the challenges posed by the aggregated scattering points of the ship target and the overlapping Doppler histories of the corner reflector array, the Clean technique is modified by short-time Fourier transform (STFT) filtering and amplitude–phase distortion correction using fractional Fourier transform (FrFT) filtering. This modification aims to improve the accuracy and efficiency of extracting scattering point components. Thirdly, in response to the poor universality of the traditional Clean iterative termination condition, the kurtosis of the residual signal spectrum amplitude is adopted as the new iterative termination condition. Compared with the existing imaging methods, the proposed method can adapt to the different Doppler distribution characteristics of the ship target and the corner reflector array, thus realizing better robustness in obtaining a well-focused target image. Finally, simulation experiments verify the effectiveness of the algorithm.

Use of a virtual phantom to assess the capability of a treatment planning system to perform magnetic resonance image distortion correction.

Treatment Planning Systems (TPS) offer algorithms for distortion correction (DC) of Magnetic Resonance (MR) images, whose performances demand proper evaluation. This work develops a procedure using a virtual phantom to quantitatively assess a TPS DC algorithm. Variations of the digital Brainweb MR study were created by introducing known distortions and Control Points (CPs). A synthetic Computed Tomography (sCT) study was created based upon the MR study. Elements TPS (Brainlab, Munich, Germany) was used to apply DC to the MR images, choosing the sCT as the gold standard. Deviations in the CP locations between the original images, the distorted images and the corrected images were calculated. Structural Similarity Metric (SSIM) tests were applied for further assessment of image corrections. The introduced distortion deviated the CP locations by a median (range) value of 1.8 (0.2-4.4) mm. After DC was applied, these values were reduced to 0.6 (0.1-1.9) mm. Correction of the original image deviated the CP locations by 0.2 (0-1.1) mm. The SSIM comparisons between the original and the distorted images yielded values of 0.23 and 0.67 before and after DC, respectively. The SSIM comparison of the original study, before and after DC, yielded a value of 0.97. The proposed methodology using a virtual phantom with CPs can be used to assess a TPS DC algorithm. Elements TPS effectively reduced MR distorsions below radiosurgery tolerances.

An All-Digital Spread Spectrum Method With Distortion Correction for Filterless Digital Class-D Amplifiers

An All-Digital Spread Spectrum Method With Distortion Correction for Filterless Digital Class-D Amplifiers

Focal volume reduction in transcranial focused ultrasound using spherical wave expansions.

Transcranial focused ultrasound (tFUS) has been gaining increased attention as a non-invasive modality for treating brain diseases. However, accurately focusing on brain structures remains a challenge as the ultrasound is severely distorted by the presence of the skull. In this article, we propose a promising distortion correction method based on spherical wave expansions. It is demonstrated that the focal gain is directly related to the zero-order spherical harmonic coefficient, and suppressing higher-order coefficients significantly reduces the focal volume. Simulation results show that this method can correct distortion and effectively balance focal gain and volume, achieving a smaller focal spot with lower grating lobes compared to the commonly used time reversal technique. We also verified the capability of shifting the focal position in real time without additional simulations. This work provides an effective approach for tFUS treatments, offering enhanced precision and reduced focal volume.

Modelling and experimental validation of DSTATCOM using a deep belief learning network with an anti-wind-up regulator

This article proposes the shunt compensation capability improvement using a deep belief learning network approach (DBLN) with anti-wind-up regulator-supported distributed static compensator (DSTATCOM). Six subnets make up this proposed DBLN controller. Three subnets for each active and reactive mass part are employed to isolate the basic component of the output current. Numerous issues such as past and normalising weight and learning rates are engaged in the DBLN-based weight-updating formula to have a superior dynamic presence, reduce the computational load and achievesfaster estimation, etc. This proposed DBLN is suggested for both proportional-integral (PI) and anti-wind-up regulator to showcase the better DC link voltage which further leads to providing better PQ improvement. This method offers excellent dynamics and resilience to outside disturbances. The suggested study is examined by simulation and experimental development using MATLAB/Simulink by a real-time interface based on a dSPACE 1104 for healthier potential regulation, potential balancing, input current harmonic distortion and PF correction under different load scenarios.

Очистка полуструктурированных и неструктурированных данных дистанционного зондирования Земли

Abstract. The problem of processing semi-structured and unstructured Earth remote sensing (ERS) data obtained by various methods, including satellites and unmanned aerial vehicles, is touched upon. The purpose of the article is to study and implement algorithms for effective purification and preprocessing of semi-structured and unstructured Earth remote sensing data. The importance of cleaning these data from noise, artifacts and errors is emphasized in order to increase their accuracy and significance in applied scientific research and practical application. Key data preprocessing techniques are considered, including noise removal, distortion correction, classification, segmentation and standardization of data, reinforcing theoretical positions with practical examples in Python using libraries such as GDAL, OpenCV and scikit-image. Examples of detection of aerospace and transport objects using machine learning and deep learning are given, emphasizing the importance of accuracy, completeness and F1-Score metrics in assessing the quality of data purification. The practical significance of the study lies in evaluating the effectiveness of data purification methods used to restore images during remote sensing of the Earth.

Color-Distortion Correction for Jilin-1 KF01 Series Satellite Imagery Using a Data-Driven Method

Color distortion is a common issue in Jilin-1 KF01 series satellite imagery, a phenomenon caused by the instability of the sensor during the imaging process. In this paper, we propose a data-driven method to correct color distortion in Jilin-1 KF01 imagery. Our method involves three key aspects: color-distortion simulation, model design, and post-processing refinement. First, we investigate the causes of color distortion and propose algorithms to simulate this phenomenon. By superimposing simulated color-distortion patterns onto clean images, we construct color-distortion datasets comprising a large number of paired images (distorted–clean) for model training. Next, we analyze the principles behind a denoising model and explore its feasibility for color-distortion correction. Based on this analysis, we train the denoising model from scratch using the color-distortion datasets and successfully adapt it to the task of color-distortion correction in Jilin-1 KF01 imagery. Finally, we propose a novel post-processing algorithm to remove boundary artifacts caused by block-wise image processing, ensuring consistency and quality across the entire image. Experimental results show that the proposed method significantly eliminates color distortion and enhances the radiometric quality of Jilin-1 KF01 series satellite imagery, offering a solution for improving its usability in remote sensing applications.

Effect of pixel size and lateral distortion on the accuracy of stitching interferometry for high-precision x-ray mirrors.

Stitching interferometry is essential for X-ray mirror metrology where lateral distortion and pixel size are critical factors influencing its accuracy. Simulations and experiments reveal that 1% error in pixel size causes a 2% deviation in the radius of curvature. After correcting pixel size, the stitching profile error of an elliptical mirror reduces from 2 μm to 250 nm (peak-to-valley). Lateral distortion correction can further reduce retrace errors. For stitching measurements using small sub-apertures, distortion correction can be minor if the average pixel size has been corrected and the actual distortion along the stitching direction is small.

Distortion correction algorithm based on absolute phase image in structured light 3D reconstruction

Abstract This paper proposes a distortion correction algorithm based on absolute phase maps for a dual-projector, single-camera structured light 3D shape measurement system. Distortion correction is performed separately for the projector-camera pairs on the left and right. By projecting a ninth-order complementary Gray code combined with the eight-step phase-shifting method onto a white board, absolute phase maps are obtained, solving the impact of radial and tangential lens distortion. A sub-pixel level distortion error lookup table is introduced to address the influence of residual error after distortion correction on measurement accuracy, improving the overall precision by at least 95.7%. Experiments demonstrate that the dual-projector, single-camera structured light 3D shape measurement system expands the system’s measurement range while reducing shadowing issues caused by single-projector illumination. The proposed distortion correction and error compensation algorithms effectively enhance the overall measurement accuracy of the system and significantly improve issues like warping caused by distortion.

Research of Hippopotamus Optimization algorithm optimized Back Propagation neural network algorithm for distortion correction in structured light point clouds

Research of Hippopotamus Optimization algorithm optimized Back Propagation neural network algorithm for distortion correction in structured light point clouds

Pattern distortion in nanoimprint lithography using UV-curable polymer stamps

Quantification of pattern distortion in nanoimprint lithography (NIL) is required when applying it to specific applications, especially those with tight tolerances. We present a systematic study on full wafer NIL distortion using soft stamps made of different carrier foils and UV-curable polymer structure layers. These errors are evaluated by overlay patterning using NIL and optical lithography on 4-in. wafers over a distance of 80 mm. Potential causes for pattern distortion and possible correction methods are discussed in terms of stamp composition and environmental impact. Pattern distortion along axes causing dimensional change is stamp dependent, and stiffer stamps show less pattern dimensional change than the softer ones. In the best case, the minimum variation is 4 ppm (ppm), and in the worst case, 252 ppm with a softer stamp. Stamp flatness and uniform contact during imprinting are important in reducing high-order pattern distortion. A maximum dimensional variation of 32 ppm in a batch run demonstrates good pattern repeatability. Long-term dimensional stability can be affected by relative humidity, with variations on the order of 100 ppm.

A depth-based land-use semantic extraction method for landscape images

Integrated aerial and ground-based observations are essential for rapid land-use classification in modern land surveys, requiring efficient analysis of large volumes of landscape images. These images often contain background noise that impedes classification accuracy and exhibit spatial compression of the main scene, which causes semantic spatial distortion as depth increases. To address these challenges, this paper proposes a depth-based land-use semantic extraction (DLSE) method that effectively reduces background noise and corrects spatial distortions. The DLSE method follows three main steps: (1) depth estimation per pixel using a multi-resolution neural network to delineate the main scene range, filtering semantic noise; (2) conversion of perspective projection to orthographic projection per pixel to correct spatial distortion; and (3) improved land-use semantic extraction through MobileViT-enhanced feature extraction in SegNet. Experimental results demonstrate that DLSE achieves a 96.84% accuracy with more detailed outputs and operates at 23 fps, positioning it as an efficient tool for automated land surveys and land-use decision-making.

The Path to Energy Savings and CO2 Emission Reductions in China's Industrial Sector from the Perspective of Factor Price Distortions Correction - Based on an Extended Capital Vintage Model

Exploring the endogenous driving mechanism of CO2 emission abatement from the perspective of factor price distortions is crucial for China to achieve the goal of carbon peaking and carbon neutrality. Despite its importance, there is a notable gap in current research as existing studies have failed to open the "black box" of factor price distortions affecting CO2 emissions. In this paper, we construct an extended vintage capital model under the imperfect factor market scenario to shed light on the intrinsic mechanism by which factor price distortions affect energy demands and CO2 emissions through the dynamic accumulation of energy-efficient capital vintage. Subsequently, based on compiling a unique "province-industry-year" three-dimensional panel dataset (from 1998 to 2013), this research further endeavors to systematically assess the energy saving and carbon reduction potentials of correcting factor price distortions in China's industrial sector. The results show that correcting factor price distortions could save the industrial sector an average of 225 million tonnes of standard coal of energy and reduce 793 million tonnes of CO2 emissions per annum over the period of the study by promoting the energy efficiency of capital vintage. Additionally, rectifying factor price distortions can lead to significant synergistic effects in reducing atmospheric pollutants as compared to aquatic pollutants.

A fisheye image correction method based on deep learning

Purpose Fisheye camera is a wide-angle camera with a large field of view, which can obtain more information than conventional cameras, but its own disadvantage of large distortion can lead to shape distortion of objects in the captured image. To correct lens distortion, most scholars use manual calibration methods. The traditional manual calibration method relies on complex data computation and specialized mathematical knowledge, but the method is complex and not universal. Considering the nonlinear features of neural networks can be used to fit the nonlinear distortion of distorted images, this paper aims to correct the distortion of a fisheye image based on convolutional neural networks. Design/methodology/approach By generating fisheye distortion images using readily available cityscape road data sets, this paper designs an effective correction model by taking advantage of the fact that the relationship between the pixel coordinates is basically stable after uniform distortion. In our training process, the corresponding distortion is firstly synthesized using the original image, the loss function is constructed using the geometric means of the Hough transform, the whole model is then trained with the help of the loss of the linear slope, and finally the predicted parameters are used to correct the fisheye image, which realizes the overall end-to-end framework. Findings Experimental results show that the method proposed in this paper outperforms similar methods in terms of correction performance. Originality/value This paper proposed a fisheye distortion correction algorithm based on convolutional neural network, which uses distortion-free images based on a generalized fisheye imaging model and maps to generate a fisheye distortion picture data set. It also builds a loss function training model based on line reconstruction error loss of Hough transforms.

Experimental Research on the Correction of Vortex Light Wavefront Distortion

Wavefront distortion occurs when vortex beams are transmitted in the atmosphere. The turbulence effect greatly affects the transmission of information, so it is necessary to use adaptive optical correction technology to correct the wavefront distortion of the vortex beam at the receiving end. In this paper, a method of vortex wavefront distortion correction based on the deep deterministic policy gradient algorithm is proposed; this is a new correction method that can effectively handle high-dimensional state and action spaces and is especially suitable for correction problems in continuous action spaces. The entire system uses adaptive wavefront correction technology without a wavefront sensor. The simulation results show that the deep deterministic policy gradient algorithm can effectively correct the distorted vortex beams and improve the mode purity, and the intensity correlation coefficient of single-mode vortex light can be increased to about 0.88 and 0.69, respectively, under weak turbulence and strong turbulence, and the intensity coefficient of weak-turbulence multi-mode vortex light can be increased to about 0.96. The experimental results also show that the adaptive correction technology based on the deep deterministic policy gradient algorithm can effectively correct the wavefront distortion of vortex light.

Identifying Behavioral Correlates to Visual Discomfort

Outside of self-report surveys, there are no proven, reliable methods to quantify visual discomfort or visually induced motion sickness symptoms when using head-mounted displays. While valuable tools, self-report surveys suffer from potential biases and low sensitivity due to variability in how respondents may assess and report their experience. Consequently, extreme visual-vestibular conflicts are generally used to induce discomfort symptoms large enough to measure reliably with surveys (e.g., stationary participants riding virtual roller coasters). An emerging area of research is the prediction of discomfort survey results from physiological and behavioral markers. However, the signals derived from experimental paradigms that are explicitly designed to be uncomfortable may not generalize to more naturalistic experiences where comfort is prioritized. In this work we introduce a custom VR headset designed to introduce significant near-eye optical distortion (i.e., pupil swim) to induce visual discomfort during more typical VR experiences. We evaluate visual comfort in our headset while users play the popular VR title Job Simulator and show that eye-tracked dynamic distortion correction improves visual comfort in a multi-session, within-subjects user study. We additionally use representational similarity analysis to highlight changes in head and gaze behavior that are potentially more sensitive to visual discomfort than surveys.

Model for restoring obstructed beam transmission in atmospheric turbulence based on BP neural network

Atmospheric turbulence and obstacles can distort rays during transmission, resulting in significant wavefront distortion and loss of optical field information. This paper employs the phase screen method to simulate the transmission characteristics of a Gaussian plane wave in turbulent conditions, establishing an obstacle grid at the receiver to represent beam obstruction. A dataset of unobstructed transmissions is used to train a Backpropagation Neural Network, constructing neurons and connection weights. By scanning optical field data systematically, the model compensates for the obstructed portions of the optical field distribution. The results are compared to unobstructed transmissions, focusing on image similarity, and demonstrate the entire process from compensation to distortion correction. Simulation results indicate that the Backpropagation Neural Network effectively compensates for optical field information loss, showcasing strong performance within a certain time scale.

Dual-biomimetic curved compound-eye camera system for multi-target distance measurement in a large field of view.

Biomimetic curved compound-eye cameras (BCCECs) have attracted great attention for their potential applications in a variety of fields such as target recognition, monitor and three-dimensional localization in military due to their unique optical properties such as large field of view (FOV) and small size. In this work, we proposed a multi-target distance measurement method based on a dual-BCCEC system in a large FOV. To guarantee the precise measurement of the distance of multiple targets, a feature point searching and matching algorithm is developed for the dual-BCCEC system to improve the localizing efficiency of common feature points. In addition, a CALibration Tag (CALTag) self-recognition calibration method is also developed to calibrate ommatidia of the BCCEC with a high efficiency. Based on these two methods, the coordinates of multiple targets with clear feature points can be obtained after the distortion correction in sub-images and thus the distances of multiple targets with clear feature points can be achieved simultaneously with a single compound-eye raw image. The experiment results show that the dual-BCCEC system has a high distant measurement accuracy with an error of less than 6.80% for at least ten different targets in the a working distance ranging from 400 to 600 m in a quite large FOV of 98°×98°. The method demonstrated in this work can pave the way for multi-targets tracking in those related areas with high security monitoring requirements.