Distortion Correction Method Research Articles

Application of Visual Technology in Fully Mechanized Coal Face

With the gradual improvement of coal mining automation, visual technology is more and more widely used in coal mine production line. Especially in fully mechanized coal face, visual technology provides a guarantee for reducing the working face personnel and remote control. Aiming at the application scene of fully mechanized coal face, the key technologies such as visual imaging algorithm, visual data compression algorithm, panoramic reconstruction technology of fully mechanized coal face and reliable video data storage technology are combed. The distortion correction, image registration, image fusion and video data compression methods based on depth learning technology in panoramic video surveillance system of fully mechanized coal face are mainly expounded. It effectively solves the problems of large bandwidth, incomplete video storage, non-intuitive working picture of shearer and uneven video splicing picture. At the same time, the development direction of video surveillance system in fully mechanized coal face is discussed.

An improved parking space recognition algorithm based on panoramic vision

In order to reduce parking difficulties caused by small parking spaces, low driver driving experience, and complex parking environment, parking assistance systems have attracted attention, but the identification of parking spaces is a key problem and technical difficulty. In this paper, an improved parking space recognition algorithm based on panoramic vision is proposed. Firstly, in the look-around image forming part, a method of distortion correction (DC) and perspective transformation (PT) based on LUT (Look Up Table) transformation is proposed to improve the processing speed of the algorithm. Then, to improve the accuracy of parking space recognition, an improved method combining rough extraction and fine matching is proposed to identify parking spaces in a look-around image. The experimental results show that the method achieves a detection rate of 97.63% under sufficient illumination and 79.77% even under insufficient illumination.

Effect of distortion corrections on the tractography quality in spinal cord diffusion-weighted imaging.

To assess the impact of a different distortion correction (DC) method and patient geometry (sagittal balance) on the quality of spinal cord tractography rendering according to different tractography approaches. Forty-four adults free of spinal cord diseases underwent cervical diffusion-weighted imaging. The phase-encoding direction was head→foot. Sequence with opposed polarities (foot→head) was acquired to perform DC. Eddy-current, motion effects, and susceptibility artifact correction methods were used for DC, and two deterministic and one probabilistic tractography approaches were evaluated using MRtrix and DSI Studio tractography software. Fiber length and number of fibers were extracted to evaluate the quality of the tractography rendering. For each subject, cervical lordosis was measured to assess patient geometry. The angle between the main direction of the spinal cord and the orientation of the acquisition box were computed at each spine level to assess acquisition geometry and define an angle threshold for which a tractography of good quality is no longer possible. There was a significant improvement in tractography quality after performing DC with susceptibility artifact correction using a deterministic approach based on tensor. Before DC, the angle threshold was defined at C6 (15.2°) compared with C7 (21.9°) after corrections, demonstrating the importance of spinal cord angulation for DC. The impact of DC on tractography quality is greatly impacted by acquisition geometry. To obtain a good-quality tractography, we propose as a future perspective to adapt the acquisition geometry to that of the patient by automatically adjusting the acquisition box.

Measurement of Three-Dimensional Characteristics of Slug Flow

Gas-liquid slug flow in horizontal pipes occurs frequently in industrial applications and is harmful to the piping systems owing to its intermittent structures. Therefore, it is essential to accurately describe the flow structure and investigate the flow mechanism. A non-intrusive fluorescence imaging method is employed to quantitatively reconstruct the slug bubble in a 15mm ID horizontal pipe in this article. Optical distortion correction method is firstly developed to improve the measurement accuracy of the instantaneous gas holdup, and then the time-average axial bubble velocity is simultaneously measured according to the feature matching of cross-sectional images. Additionally, the influence of phase superficial velocity on the interface stability is investigated through the phase holdup and the circumferential curvature distribution analysis. The typical three-dimensional structures of slug bubble are finally reconstructed through the velocity-based bicubic interpolation and median fusion algorithm. Previous correlations and reference instrumentation are used to evaluate the proposed method. Comparison results show that the method can effectively measure the flow parameters and reconstruct slug bubbles.

1J3-2 High-speed Photoacoustic Microscopy Using MEMS Mirror with a Correction Method for Distortion Caused by the Mirror Scanning

1J3-2 High-speed Photoacoustic Microscopy Using MEMS Mirror with a Correction Method for Distortion Caused by the Mirror Scanning

Research on Intelligent Image Recognition Technology Based on Equalization Algorithm

In the image edge distortion correction, the straight-line projection-derived edge fit is poor, resulting in large correction errors. Therefore, an image edge distortion correction algorithm based on the equalization algorithm is proposed in the paper, where the adaptive threshold wavelet denoising algorithm is adopted to implement adaptive transformation of various scale parameters to complete noise removal. According to the results of image denoising, Fisher vector coding is used to optimize the results of the equalization algorithm to extract the edge distortion of the image. Besides, based on the edge distortion shape extraction results, the correction objective optimization function is obtained to analyze the edge fracture situation, realizing the linear projection derivative edge fitting. Meanwhile, by determining the image edge error evaluation function, the method of image edge distortion correction is judged to achieve the purpose of image edge distortion correction. Therefore, taking noisy barrel distortion and pincushion distortion images as the research object, the experimental analysis is carried out. The results show that the algorithm proposed in the paper can effectively correct the barrel distortion and pincushion distortion of the image, achieving high-precision and high-efficiency image edge distortion correction.

Distortion measurement and correction method on compound-eye optical imaging system

复眼式光学成像系统在大视场侦查、图像识别、目标探测等领域较传统单孔径光学系统优势突出，但随着视场的增加，子孔径本身的成像畸变及多个子孔径的安装位置误差引起的畸变会直接影响拼接图像的质量。针对该问题，采用光电测量技术对复眼系统进行畸变测量与校正，生成多模动态电子畸变测量靶标，构建畸变测量校正模型，建立多项式拟合算法，采用最小二乘法获得畸变系数，通过双线性插值法模型对图像进行重建。实验结果表明，校正后的平均相对畸变优于0.1%，满足大视场复眼式光学成像系统的畸变校正和图像拼接的精度要求。

Non-Metric Distortion Correction Method Based on Adaptive Corner Detection

Non-Metric Distortion Correction Method Based on Adaptive Corner Detection

Evaluation of patient-specific MR distortion correction schemes for improved target localization accuracy in SRS.

This work aims at promoting target localization accuracy in cranial stereotactic radiosurgery (SRS) applications by focusing on the correction of sequence-dependent (also patient induced) magnetic resonance (MR) distortions at the lesion locations. A phantom-based quality assurance (QA) methodology was developed and implemented for the evaluation of three distortion correction techniques. The same approach was also adapted to cranial MR images used for SRS treatment planning purposes in single or multiple brain metastases cases. A three-dimensional (3D)-printed head phantom was filled with a 3D polymer gel dosimeter. Following treatment planning and dose delivery, volumes of radiation-induced polymerization served as hypothetical lesions, offering adequate MR contrast with respect to the surrounding unirradiated areas. T1-weighted (T1w) MR imaging was performed at 1.5T using the clinical scanning protocol for SRS. Additional images were acquired to implement three distortion correction methods; the field mapping (FM), mean image (MI) and signal integration (SI) techniques. Reference lesion locations were calculated as the averaged centroid positions of each target identified in the forward and reverse read gradient polarity MRI scans. The same techniques and workflows were implemented for the correction of contrast-enhanced T1w MR images of 10 patients with a total of 27 brain metastases. All methods employed in the phantom study diminished spatial distortion. Median and maximum distortion magnitude decreased from 0.7mm (2.10ppm) and 0.8mm (2.36ppm), respectively, to <0.2mm (0.61ppm) at all target locations, using any of the three techniques. Image quality of the corrected images was acceptable, while contrast-to-noise ratio slightly increased. Results of the patient study were in accordance with the findings of the phantom study. Residual distortion in corrected patient images was found to be <0.3mm in the vast majority of targets. Overall, the MI approach appears to be the most efficient correction method from the three investigated. In cranial SRS applications, patient-specific distortion correction at the target location(s) is feasible and effective, despite the expense of longer imaging time since additional MRI scan(s) need to be performed. A phantom-based QA methodology was developed and presented to reassure efficient implementation of correction techniques for sequence-dependent spatial distortion.

A scanning distortion correction method based on X – Y galvanometer Lidar system* *Project supported by the National Natural Science Foundation of China (Grant Nos. 61775048 and 62027823) and the Natural Science Foundation of Shenzhen (Grant No. JCYJ2020109150808037).

Aiming at the problem of scanning distortion in X–Y galvanometer light detecting and ranging (Lidar) scanning system, we propose a method of image scanning distortion correction with controllable driving voltage compensation. Firstly, the geometrical optics vectors model is established to explain the principle of pincushion distortion in the galvanometer scanning system, and the simulation result of scanning trajectory is consistent with experiments. The linear relationship between the driving voltage and the scanning angle of the galvanometer is verified. Secondly, the relationship between the deflection angle of the galvanometer and the scanning trajectory and the driving voltage is deduced respectively, and an image scanning correction algorithm with controllable driving voltage compensation is obtained. The simulation experiment results of the proposed method show that the root-mean-square error (RMSE) and the corresponding curve between the scan value and the actual value at different distances, have a good correction effect for the pincushion distortion. Finally, the X–Y galvanometer scanning Lidar system is established to obtain undistorted two-dimensional scanned image and it can be applied to the three-dimensional Lidar scanning system in the actual experiments, which further demonstrates the feasibility and practicability of our method.

A method of wavefront distortions correction for an atmospheric optical link with a small volume of information transmitted through a service channel

The paper describes a method of wavefront distortion correction in a turbulent atmosphere, which allows reducing the volume of information transmitted through a service channel. The correction is carried out with a transparency whose phase mask is calculated by a simple three-layer neural network by intensity distribution obtained at the photoreceiver. The architecture of the network is constructed so that the amount of data transferred over the service channel is reduced to 30 parameters, using which a corrective mask is then constructed.

Synchronous Fingerprint Acquisition System Based on Total Internal Reflection and Optical Coherence Tomography

The research of external fingerprint collected by total internal reflection (TIR) has been carried out for decades and the research of internal fingerprint collected by optical coherence tomography (OCT) has just begun. The internal fingerprint can be hardly affected by the finger surface status, due to its strong antiinterference and antispoofing ability, which can serve as a powerful supplement to external fingerprint. However, matching fingerprints acquired in different ways can lead to a drop in fingerprint recognition accuracy due to the differences in fingerprint quality, distortions, and detection areas. Whether the internal fingerprint can be used to replace the external fingerprint for direct identification has been hampered by lacking comparison tools to study potential correlation with each other. To study the connection between internal and external fingerprints, a synchronous acquisition system that achieves the same fingerprint area measurement at the same time is necessary. The integration of different optical paths of TIR and OCT as well as the consequent distortion correction of two totally different imaging ways are two challenging problems. This article presents a fingerprint acquisition system that synchronously acquires the external fingerprint and internal fingerprint with TIR and OCT, respectively. By using a self-designed trapezoidal prism, optical paths of TIR and OCT were integrated. Furthermore, a distortion correction method based on a grid calibration plate is used to remove distortions caused by two imaging methods. The fingerprint quality statistics are illustrated in 264 sets of fingerprint data sets. Identification results show synchronous acquisition and distortion correction of the proposed system are efficient.

Dynamic distortion correction for functional MRI using FID navigators.

To develop a method for slice-wise dynamic distortion correction for EPI using rapid spatiotemporal B0 field measurements from FID navigators (FIDnavs) and to evaluate the efficacy of this new approach relative to an established data-driven technique. A low-resolution reference image was used to create a forward model of FIDnav signal changes to enable estimation of spatiotemporal B0 inhomogeneity variations up to second order from measured FIDnavs. Five volunteers were scanned at 3 T using a 64-channel coil with FID-navigated EPI. The accuracy of voxel shift measurements and geometric distortion correction was assessed for experimentally induced magnetic field perturbations. The temporal SNR was evaluated in EPI time-series acquired at rest and with a continuous nose-touching action, before and after image realignment. Field inhomogeneity coefficients and voxel shift maps measured using FIDnavs were in excellent agreement with multi-echo EPI measurements. The FID-navigated distortion correction accurately corrected image geometry in the presence of induced magnetic field perturbations, outperforming the data-driven approach in regions with large field offsets. In functional MRI scans with nose touching, FIDnav-based correction yielded temporal SNR gains of 30% in gray matter. Following image realignment, which accounted for global image shifts, temporal SNR gains of 3% were achieved. Our proposed application of FIDnavs enables slice-wise dynamic distortion correction with high temporal efficiency. We achieved improved signal stability by leveraging the encoding information from multichannel coils. This approach can be easily adapted to other EPI-based sequences to improve temporal SNR for a variety of clinical and research applications.

The impact of EPI-based distortion correction of dynamic susceptibility contrast MRI on cerebral blood volume estimation in patients with glioblastoma

PurposeRelative cerebral blood volume (rCBV) from dynamic susceptibility contrast (DSC)-MRI is a valuable biomarker in patients with glioblastoma for assessing treatment response and predicting overall survival. DSC-MRI based on echo planar images (EPI) may possess severe geometric distortions from magnetic field inhomogeneities up to the order of centimeters. The aim of this study is to assess how much two readily available EPI-based geometric distortion correction methods, FSL TOPUP and EPIC, affect rCBV values from DSC-MRI in patients with confirmed glioblastoma. MethodWe used a combined single-shot 2D gradient-echo (T2*), spin-echo (T2) EPI sequence to estimate both T2* and T2-weighted rCBV from the same contrast agent injection. Effects of distortion correction on the positive phase-encoded T2- and T2*-images were assessed in healthy anatomical brain regions in terms of Wilcoxon signed rank tests on median rCBV change and on Dice coefficients, as well as in tumor lesions in terms of Wilcoxon signed rank tests on median rCBV change. ResultsOur results show that following distortion correction, both gradient-echo and spin-echo rCBV increased in cortical areas of the frontal, temporal and occipital lobe, including the posterior orbital gyri in the frontal lobe and middle frontal gyri (p < 0.0008). Similar, improved Dice coefficients were observed for gradient-echo EPI in temporal, occipital and frontal lobe. Only spin-echo rCBV in enhancing lesion increased with correction (p = 0.0002). ConclusionOur study sheds light on the importance of performing geometric distortion correction on EPI-based MRI data before assessing functional information such as rCBV values. Our findings may indicate that uncorrected rCBV values can be underestimated from positive phase-encoding EPI and that geometric distortion correction is warranted when comparing EPI-based data to conventional MRI.

Adaptive Fine Distortion Correction Method for Stereo Images of Skin Acquired with a Mobile Phone.

With the development of the mobile phone, we can acquire high-resolution images of the skin to observe its detailed features using a mobile camera. We acquire stereo images using a mobile camera to enable a three-dimensional (3D) analysis of the skin surface. However, geometric changes in the observed skin structure caused by the lens distortion of the mobile phone result in a low accuracy of the 3D information extracted through stereo matching. Therefore, our study proposes a Distortion Correction Matrix (DCM) to correct the fine distortion of close-up mobile images, pixel by pixel. We verified the correction performance by analyzing the results of correspondence point matching in the stereo image corrected using the DCM. We also confirmed the correction results of the image taken at the five different working distances and derived a linear regression model for the relationship between the angle of the image and the distortion ratio. The proposed DCM considers the distortion degree, which appears to be different in the left and right regions of the image. Finally, we performed a fine distortion correction, which is difficult to check with the naked eye. The results of this study can enable the accurate and precise 3D analysis of the skin surface using corrected mobile images.

Distortion correction of diffusion weighted MRIwithout reverse phase-encoding scans or field-maps.

Diffusion magnetic resonance images may suffer from geometric distortions due to susceptibility induced off resonance fields, which cause geometric mismatch with anatomical images and ultimately affect subsequent quantification of microstructural or connectivity indices. State-of-the art diffusion distortion correction methods typically require data acquired with reverse phase encoding directions, resulting in varying magnitudes and orientations of distortion, which allow estimation of an undistorted volume. Alternatively, additional field maps acquisitions can be used along with sequence information to determine warping fields. However, not all imaging protocols include these additional scans and cannot take advantage of state-of-the art distortion correction. To avoid additional acquisitions, structural MRI (undistorted scans) can be used as registration targets for intensity driven correction. In this study, we aim to (1) enable susceptibility distortion correction with historical and/or limited diffusion datasets that do not include specific sequences for distortion correction and (2) avoid the computationally intensive registration procedure typically required for distortion correction using structural scans. To achieve these aims, we use deep learning (3D U-nets) to synthesize an undistorted b0 image that matches geometry of structural T1w images and intensity contrasts from diffusion images. Importantly, the training dataset is heterogenous, consisting of varying acquisitions of both structural and diffusion. We apply our approach to a withheld test set and show that distortions are successfully corrected after processing. We quantitatively evaluate the proposed distortion correction and intensity-based registration against state-of-the-art distortion correction (FSL topup). The results illustrate that the proposed pipeline results in b0 images that are geometrically similar to non-distorted structural images, and more closely match state-of-the-art correction with additional acquisitions. In addition, we show generalizability of the proposed approach to datasets that were not in the original training / validation / testing datasets. These datasets included varying populations, contrasts, resolutions, and magnitudes and orientations of distortion and show efficacious distortion correction. The method is available as a Singularity container, source code, and an executable trained model to facilitate evaluation.

Distortion correction of single-shot EPI enabled by deep-learning

PurposeA distortion correction method for single-shot EPI was proposed. Point-spread-function encoded EPI (PSF-EPI) images were used as the references to correct traditional EPI images based on deep neural network. Theory and methodsThe PSF-EPI method can obtain distortion-free echo planar images. In this study, a 2D U-net based network was trained to achieve the distortion correction of single-shot EPI (SS-EPI) images, using PSF-EPI images as targets in the training stage. Anatomical T2W-TSE images were also fed into the network to improve the quality of the results. The applications in diffusion-weighted images were used as examples in this work. The network was trained on data acquired on healthy volunteers and tested on data of both healthy volunteers and patients. The corrected EPI images from the proposed method were also compared with those from field-mapping and top-up based distortion correction methods. ResultsExperimental results showed that the proposed method can correct for EPI distortions better than both the field-mapping and top-up based methods, and the results were close to the distortion-free images from PSF-EPI. Additionally, inclusion of T2W-TSE images helped improve distortion correction of the SS-EPI images without contaminating the output noticeably. The experiments with patients and different MRI platforms demonstrated the generalization feasibility of the proposed method preliminarily. ConclusionThrough the correction of diffusion-weighted images, the proposed deep-learning based method was demonstrated to have the feasibility to correct for the distortion of EPI images.

Diffusion-weighted breast magnetic resonance imaging with distortion correction using non-rigid image registration: a clinical study

Our distortion correction method for diffusion-weighted breast images was applied to clinical studies to evaluate its practicality. We used b values of 0 and 1500s/mm2 and dynamic images captured after injecting the contrast medium for magnetic resonance imaging (MRI). This method is based on non-rigid image registration, and dispenses with multiple scans, reducing examination time and patient burden. Image correction included the following: matrix size matching; segmentation; edge detection; non-rigid image registration between T2-weighted images with fat suppression and b0 images; and image warping of b1500 images. Significant differences were noted for all combinations of cross-correlation coefficients ofwhole-breast images betweenwith and without correction, and no significant differences for apparent diffusion coefficients. The percentage differences were not significant for cross-correlation coefficients, with and without correction, calculated for various sizes, positions, and types of lesions of high-intensity regions. In conclusion, our distortion correction method is effective for clinical breast MRI.

眼科手术导航的OCT图像畸变矫正

眼科手术导航的OCT图像畸变矫正

A method of perspective normalization for video images based on map data

ABSTRACTObjective perspective distortion is a problem that needs to be solved by video surveillance analysis. Compared with the street scene method, which depends on prior knowledge of the scene or 3D scene of the dedicated hardware recovery scene, the commonly used perspective distortion correction method is based on the linear relationship to monitor a video image in perspective normalization. However, the distortion caused by perspective imaging is nonlinear, and the linear perspective normalization model cannot guarantee the accuracy of the correction in the scene where the perspective phenomenon is evident. An image normalization method based on map data is proposed to solve this problem. A nonlinear perspective correction model is introduced by establishing a single relation between video image space and map space. With selected control points between image and map, we can calculate homography matrix in order to build the perspective correction model, which is computed to know the single pixel real size in map. The proposed perspective correction model is applied to the moving target detection. The results of the linear correction model and the proposed nonlinear correction model prove the validity and practicability of the method.