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The Lightning Mapping Imager (LMI) onboard the Fengyun-4A (FY-4A) satellite is the first independently developed satellite-borne lightning imager in China. It enables continuous lightning detection in China and surrounding areas, regardless of weather conditions. The FY-4A LMI uses a Charge-Coupled Device (CCD) array for lightning detection, and the accuracy of lightning positioning is influenced by cloud top height (CTH). In this study, we proposed an ellipsoid CTH parallax correction (ECPC) model for lightning positioning applicable to FY-4A LMI. The model utilizes CTH data from the Advanced Geosynchronous Radiation Imager (AGRI) on FY-4A to correct the lightning positioning data. According to the model, when the CTH is 12 km, the maximum deviation in lightning positioning caused by CTH in Beijing is approximately 0.1177° in the east–west direction and 0.0530° in the north–south direction, corresponding to a horizontal deviation of 13.1558 km, which exceeds the size of a single ground detection unit of the geostationary satellite lightning imager. Therefore, it is necessary to be corrected. A comparison with data from the Beijing Broadband Lightning Network (BLNET) and radar data shows that the corrected LMI data exhibit spatial distribution that is closer to the simultaneous BLNET lightning positioning data. The coordinate differences between the two datasets are significantly reduced, indicating higher consistency with radar data. The correction algorithm decreases the LMI lightning location deviation caused by CTH, thereby improving the accuracy and reliability of satellite lightning positioning data. The proposed ECPC model can be used for the real-time correction of lightning data when CTH is obtained at the same time, and it can be also used for the post-correction of space-based lightning detection with other cloud top height data.

Parallax is associated with an apparent shift of the position of an object when viewed from different angles. For satellite observations, especially observations with clouds, it affects collocation of measurements from different platforms. In this study, we investigate how the parallax problem affects the collocation of CloudSat and Moderate Resolution Imaging Spectroradiometer (MODIS) observations of tropical convective clouds by examining the impact of parallax correction on statistics of convective cloud properties such as cloud top temperature (CTT) and buoyancy. Previous studies circumvented the parallax problem by imposing a “flat‐top” condition on the selection of convective clouds, but it inadvertently biases the statistics toward convections at mature or dissipating stages when convective plumes cease to grow but flatten out to form cirrus anvils. The main findings of this study are the following: (1) Parallax correction reduces CTT of convective clouds; the magnitude of the reduction increases with cloud top height (CTH). (2) Parallax correction also reduces the spread of CTT estimates, making it more closely clustered around the corresponding CTH. (3) The fraction of convection with positive buoyancy decreases after the parallax correction. All these changes that are due to parallax correction are most pronounced for convections above 10–12 km, highlighting the importance of parallax correction in satellite‐based study of deep convection. With parallax correction applied, we further examine the contrast in convective cloud buoyancy between land and ocean and day and night and the dependence on convective cloud size; results are consistent with our general understanding of tropical convection. Key Points Parallax correction is important for spaceborne studies of convective clouds Parallax correction here improves the estimates of cloud top temperature Parallax correction affects relevant statistics in satellite data analysis

This chapter contains sections titled: Advantages of PET imaging PET camera components Factors affecting resolution in PET imaging Attenuation in PET imaging Standard uptake values References Questions Answers

Camera arrays typically use image-stitching algorithms to generate wide field-of-view panoramas, but parallax and color differences caused by varying viewing angles often result in noticeable artifacts in the stitching result. However, existing solutions can only address specific color difference issues and are ineffective for pinhole images with parallax. To overcome these limitations, we propose a parallax-tolerant weakly supervised pixel-wise deep color correction framework for the image stitching of pinhole camera arrays. The total framework consists of two stages. In the first stage, based on the differences between high-dimensional feature vectors extracted by a convolutional module, a parallax-tolerant color correction network with dynamic loss weights is utilized to adaptively compensate for color differences in overlapping regions. In the second stage, we introduce a gradient-based Markov Random Field inference strategy for correction coefficients of non-overlapping regions to harmonize non-overlapping regions with overlapping regions. Additionally, we innovatively propose an evaluation metric called Color Differences Across the Seam to quantitatively measure the naturalness of transitions across the composition seam. Comparative experiments conducted on popular datasets and authentic images demonstrate that our approach outperforms existing solutions in both qualitative and quantitative evaluations, effectively eliminating visible artifacts and producing natural-looking composite images.

Satellite-derived global horizontal irradiance (GHI) is an excellent data source for nowcasting solar power generation and validating weather and climate models. To obtain a good match between satellite-derived GHI and surface observations of GHI, precise geolocation of the satellite GHI is an essential factor in addition to the accuracy of the retrieval. The geolocation of satellite retrievals is affected by parallax, a displacement between the actual and apparent position of a cloud, as well as by a displacement between the actual position of a shadow and the retrieved position of the shadow, which, due to the one-dimensional (1D) radiative transfer assumption, is directly below the cloud. This study evaluates different approaches to correcting Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) retrievals for parallax and cloud shadow displacements using ground-based observations from a unique network of 99 pyranometers deployed during the HD(CP)2 Observational Prototype Experiment (HOPE) field campaign in Jülich, Germany, in 2013. The first method provides geometric corrections for the displacements calculated using retrieved cloud top heights (Hc). The second method relies on empirical collocation shifting. Here, the collocation shift of the satellite grid is determined by maximizing the correlation between the satellite retrievals and ground-based observations. This optimum shift is determined either based on daily or time-step-averaged correlations. The time-step-averaged collocation shift correction generally yields the most accurate results, but a major drawback of this method is its reliance on ground measurements. The geometric correction, which does not have this disadvantage, achieves the most accurate results if a combined parallax and shadow correction is performed. It reduces the GHI root mean square error (RMSE) by 11.7 W m−2 (10.8 %) compared to the uncorrected retrieval. Separate parallax or shadow corrections do not reach this level of accuracy. In fact, depending on the cloud regime, they may even increase the error compared to the uncorrected retrieval. In some cases, particularly when multilevel clouds are present, the retrieval accuracy improves if the geometric correction is based on a reduced Hc. Finally, it is demonstrated that GHI becomes increasingly sensitive to the applied correction at higher spatial resolutions, especially for variable cloud regimes. This has important implications for the retrieval accuracy of the current generation of geostationary satellites with spatial resolutions down to 500 m.

In this paper, we propose a unified framework to generate a pleasant and high-quality street-view panorama by stitching multiple panoramic images captured from the cameras mounted on the mobile platform. Our proposed framework is comprised of four major steps: image warping, color correction, optimal seam line detection and image blending. Since the input images are captured without a precisely common projection center from the scenes with the depth differences with respect to the cameras to different extents, such images cannot be precisely aligned in geometry. Therefore, an efficient image warping method based on the dense optical flow field is proposed to greatly suppress the influence of large geometric misalignment at first. Then, to lessen the influence of photometric inconsistencies caused by the illumination variations and different exposure settings, we propose an efficient color correction algorithm via matching extreme points of histograms to greatly decrease color differences between warped images. After that, the optimal seam lines between adjacent input images are detected via the graph cut energy minimization framework. At last, the Laplacian pyramid blending algorithm is applied to further eliminate the stitching artifacts along the optimal seam lines. Experimental results on a large set of challenging street-view panoramic images captured form the real world illustrate that the proposed system is capable of creating high-quality panoramas.

In this paper, a framework of obstacle avoidance algorithm applied to power line damage safety distance detection is constructed, and its overall architecture and key processes are described in detail. The system design covers three core modules: visual data acquisition and preliminary processing, accurate target recognition and distance measurement, and system error analysis and correction. In the visual data processing chain, we deeply analyze every step from image acquisition to preprocessing to feature extraction, aiming to enhance the adaptability of applications to complex scenes. The target recognition and distance estimation part integrates advanced technology of deep learning to improve the reliability of recognition accuracy and distance estimation. In addition, many common error sources, such as system bias, parallax discontinuity, fluctuation of illumination conditions, etc., are discussed in depth, and corresponding correction strategies are proposed to ensure the accuracy and stability of the system, which provides powerful technical support for achieving efficient and accurate safety monitoring. Specifically, by carefully adjusting the learning rate, convolution kernel size, batch size, pooling layer type, and number of hidden layer nodes, we succeeded in improving the overall accuracy from the initial average of 92.4–95%, and the error rate decreased accordingly.

Three-dimensional reconstruction technology based on binocular stereo vision is a key research area with potential clinical applications. Mainstream research has focused on sparse point reconstruction within the soft tissue domain, limiting the comprehensive 3D data acquisition required for effective surgical robot navigation. This study introduces a new paradigm to address existing challenges. An innovative stereoscopic endoscopic image correction algorithm is proposed, exploiting intrinsic insights into stereoscopic calibration parameters. The synergy between the stereoscopic endoscope parameters and the disparity map derived from the cardiac soft tissue images ultimately leads to the acquisition of precise 3D points. Guided by deliberate filtering and optimization methods, the triangulation process subsequently facilitates the reconstruction of the complex surface of the cardiac soft tissue. The experimental results strongly emphasize the accuracy of the calibration algorithm, confirming its utility in stereoscopic endoscopy. Furthermore, the image rectification algorithm exhibits a significant reduction in vertical parallax, which effectively enhances the stereo matching process. The resulting 3D reconstruction technique enables the targeted surface reconstruction of different regions of interest in the cardiac soft tissue landscape. This study demonstrates the potential of binocular stereo vision-based 3D reconstruction techniques for integration into clinical settings. The combination of joint calibration algorithms, image correction innovations, and precise tissue reconstruction enhances the promise of improved surgical precision and outcomes in the field of cardiac interventions.

Image stitching is the process of stitching several images that overlap each other into a single, larger image. The traditional image stitching algorithm searches the feature points of the image, performs alignments, and constructs the projection transformation relationship. The traditional algorithm has a strong dependence on feature points; as such, if feature points are sparse or unevenly distributed in the scene, the stitching will be misaligned or even fail completely. In scenes with obvious parallaxes, the global homography projection transformation relationship cannot be used for image alignment. To address these problems, this paper proposes a method of image stitching based on fixed camera positions and a hierarchical projection method based on depth information. The method does not depend on the number and distribution of feature points, so it avoids the complexity of feature point detection. Additionally, the effect of parallax on stitching is eliminated to a certain extent. Our experiments showed that the proposed method based on the camera calibration model can achieve more robust stitching results when a scene has few feature points, uneven feature point distribution, or significant parallax.

This chapter contains sections titled: Common Biases High Versus Low Values of the Hubble Constant: Science or Philosophy? Bibliography