Image Tilt Research Articles

An approach for coherent periodogram averaging of tilt-series data for improved CTF estimation.

Cryo-electron microscopy (cryo-EM) has become an indispensable technique for determining three-dimensional structures of biological macromolecules. A critical aspect of achieving high-resolution cryo-EM reconstructions is accurately determining and correcting for the microscope's contrast transfer function (CTF). The CTF introduces defocus-dependent distortions during imaging; if not properly accounted for, the CTF can distort features in and limit the resolution of 3D reconstructions. For tilt-series data used in cryo-electron tomography (cryo-ET), CTF estimation becomes even more challenging due to the tilt of the specimen, which introduces a defocus gradient across the field of view, as well as the low dose and signal in individual tilt images. Here, we describe a simple algorithm to improve the accuracy of CTF estimation of tilted images by leveraging the tilt-series alignment parameters determined for tomographic reconstruction to explicitly account for the tilted specimen geometry. In brief, each tilt image is divided into patches, each of which are then stretched according to their defocus shift. These are then summed to provide a coherent power spectra at the tilt axis, which can then be used in standard CTF estimation algorithms. This uses all the data in each image to enhance the visibility of Thon rings, thereby improving high-resolution CTF estimation and subsequent enhancements in the resolution of subtomogram averages.

An Adjustment Strategy for Tilted Moiré Fringes via Deep Q-Network

Overlay accuracy, one of the three fundamental indicators of lithography, is directly influenced by alignment precision. During the alignment process based on the Moiré fringe method, a slight angular misalignment between the mask and wafer will cause the Moiré fringes to tilt, thereby affecting the alignment accuracy. This paper proposes a leveling strategy based on the DQN (Deep Q-Network) algorithm. This strategy involves using four consecutive frames of wafer tilt images as the input values for a convolutional neural network (CNN), which serves as the environment model. The environment model is divided into two groups: the horizontal plane tilt environment model and the vertical plane tilt environment model. After convolution through the CNN and training with the pooling operation, the Q-value consisting of n discrete actions is output. In the DQN algorithm, the main contributions of this paper lie in three points: the adaptive application of environmental model input, parameter optimization of the loss function, and the possibility of application in the actual environment to provide some ideas. The environment model input interface can be applied to different tilt models and more complex scenes. The optimization of the loss function can match the leveling of different tilt models. Considering the application of this strategy in actual scenarios, motion calibration and detection between the mask and the wafer provide some ideas. To verify the reliability of the algorithm, simulations were conducted to generate tilted Moiré fringes resulting from tilt angles of the wafer plate, and the phase of the tilted Moiré fringes was subsequently calculated. The angle of the wafer was automatically adjusted using the DQN algorithm, and then various angles were measured. Repeated measurements were also conducted at the same angle. The angle deviation accuracy of the horizontal plane tilt environment model reached 0.0011 degrees, and the accuracy of repeated measurements reached 0.00025 degrees. The angle deviation accuracy of the vertical plane tilt environment model reached 0.0043 degrees, and repeated measurements achieved a precision of 0.00027 degrees. Moreover, in practical applications, it also provides corresponding ideas to ensure the determination of the relative position between the mask and wafer and the detection of movement, offering the potential for its application in the industry.

A lightweight detection method of pavement potholes based on binocular stereo vision and deep learning

The identification of pavement defects is crucial and must be done efficiently, accurately, and cost-effectively. A commercial-grade sports camera and mobile vehicle work together to detect pavement potholes in a lightweight manner. A dataset of 6,186 images with a resolution of 1500 by 1500 pixels has been constructed based on vehicle-mounted tilted images. Object detection and image segmentation are performed using a single-model MASK R-CNN, and the principle of binocular stereo vision is employed to reconstruct the three-dimensional (3D) structures of potholes. A novel methodology is introduced to calculate the 3D feature parameters of potholes, enabling the precise determination of the pavement pothole damage ratio. An average accuracy of 98% for detection and 94% for segmentation is achieved with the Mask R-CNN model. The proposed algorithms achieve an average DR calculation accuracy of 82%, facilitating precise identification of surface irregularities. The paper presents an intelligent approach for detecting pavement potholes.

Pillar data-acquisition strategies for cryo-electron tomography of beam-sensitive biological samples.

For cryo-electron tomography (cryo-ET) of beam-sensitive biological specimens, a planar sample geometry is typically used. As the sample is tilted, the effective thickness of the sample along the direction of the electron beam increases and the signal-to-noise ratio concomitantly decreases, limiting the transfer of information at high tilt angles. In addition, the tilt range where data can be collected is limited by a combination of various sample-environment constraints, including the limited space in the objective lens pole piece and the possible use of fixed conductive braids to cool the specimen. Consequently, most tilt series are limited to a maximum of ±70°, leading to the presence of a missing wedge in Fourier space. The acquisition of cryo-ET data without a missing wedge, for example using a cylindrical sample geometry, is hence attractive for volumetric analysis of low-symmetry structures such as organelles or vesicles, lysis events, pore formation or filaments for which the missing information cannot be compensated by averaging techniques. Irrespective of the geometry, electron-beam damage to the specimen is an issue and the first images acquired will transfer more high-resolution information than those acquired last. There isalso an inherent trade-off between higher sampling in Fourier space and avoiding beam damage to the sample. Finally, the necessity of using a sufficient electron fluence to align the tilt images means that this fluence needs to be fractionated across a small number of images; therefore, the order of data acquisition is also a factor to consider. Here, an n-helix tilt scheme is described and simulated which uses overlapping and interleaved tilt series to maximize the use of a pillar geometry, allowing the entire pillar volume to be reconstructed asa single unit. Three related tilt schemes are also evaluated that extend the continuous and classic dose-symmetric tilt schemes for cryo-ET to pillar samples to enable the collection of isotropic information across all spatial frequencies. Afourfold dose-symmetric scheme is proposed which provides a practical compromise between uniform information transfer and complexity of data acquisition.

Adaptation of Postural Sway in a Standing Position during Tilted Video Viewing Using Virtual Reality: A Comparison between Younger and Older Adults.

This study aimed to investigate the effects of wearing virtual reality (VR) with a head-mounted display (HMD) on body sway in younger and older adults. A standing posture with eyes open without an HMD constituted the control condition. Wearing an HMD and viewing a 30°-tilt image and a 60°-tilt image in a resting standing position were the experimental conditions. Measurements were made using a force plate. All conditions were performed three times each and included the X-axis trajectory length (mm), Y-axis trajectory length (mm), total trajectory length (mm), trajectory length per unit time (mm/s), outer peripheral area (mm2), and rectangular area (mm2). The results showed a significant interaction between generation and condition in Y-axis trajectory length (mm) and total trajectory length (mm), with an increased body center-of-gravity sway during the viewing of tilted VR images in older adults than in younger adults in both sexes. The results of this study show that body sway can be induced by visual stimulation alone with VR without movement, suggesting the possibility of providing safe and simple balance training to older adults.

QR Code Recognition Based on Image Processing

To solve the QR code recognition problem caused by ordinary camera collection, the recognition algorithm based on image processing is put forward in this paper. The whole process including image binarization, image tilt correction, image orientation, image geometric correction and image normalization allows images collected on different illumination conditions. Experiments show that the improved method can enhance the recognition speed of two-dimensional code and accuracy. QR i.e. “Quick Response” code is a 2D matrix code that is designed by keeping two points under consideration, i.e. it must store large amount of data as compared to 1D barcodes and it must be decoded at high speed using any handheld device like phones. QR code provides high data storage capacity, fast scanning, omnidirectional readability, and many other advantages including, error-correction (so that damaged code can also be read successfully) and different type of versions. Different varieties of QR code symbols like logo QR code, encrypted QR code, QR Code are also available so that user can choose among them according to their need. Now these days, a QR code is applied in different application streams related to marketing, security, academics etc. and gain popularity at a really high pace. Day by day more people are getting aware of this technology and use it accordingly. The popularity of QR code grows rapidly with the growth of smartphone users and thus the QR code is rapidly arriving at high levels of acceptance worldwide.

Brain-like illusion produced by Skye's Oblique Grating in deep neural networks.

The analogy between the brain and deep neural networks (DNNs) has sparked interest in neuroscience. Although DNNs have limitations, they remain valuable for modeling specific brain characteristics. This study used Skye's Oblique Grating illusion to assess DNNs' relevance to brain neural networks. We collected data on human perceptual responses to a series of visual illusions. This data was then used to assess how DNN responses to these illusions paralleled or differed from human behavior. We performed two analyses:(1) We trained DNNs to perform horizontal vs. non-horizontal classification on images with bars tilted different degrees (non-illusory images) and tested them on images with horizontal bars with different illusory strengths measured by human behavior (illusory images), finding that DNNs showed human-like illusions; (2) We performed representational similarity analysis to assess whether illusory representation existed in different layers within DNNs, finding that DNNs showed illusion-like responses to illusory images. The representational similarity between real tilted images and illusory images was calculated, which showed the highest values in the early layers and decreased layer-by-layer. Our findings suggest that DNNs could serve as potential models for explaining the mechanism of visual illusions in human brain, particularly those that may originate in early visual areas like the primary visual cortex (V1). While promising, further research is necessary to understand the nuanced differences between DNNs and human visual pathways.

Preparation of Bunyavirus-Infected Cells for Electron Cryo-Tomography.

Cellular electron cryo-tomography (cryoET) produces high-resolution three-dimensional images of subcellular structures in a near-native frozen-hydrated state. These three-dimensional images are obtained by recording a series of two-dimensional tilt images on a transmission electron cryo-microscope that are subsequently back-projected to form a tomogram. Key to a successful experiment is however a high-quality sample. This chapter outlines a basic workflow for the preparation of cellular cryoET samples. It covers the preparation of infected cells on electron cryo-microscopy grids and the vitrification by plunge-freezing and clipping of grids into AutoGrid rims. It also provides a general overview of the workflow for thinning the vitrified cells by focused ion beam (FIB) milling. Although this book is dedicated to Rift Valley fever virus research, the present protocol may also be applied to any other research subject where high-resolution structural insight into intracellular processes is desired.

3D Imaging of PEMFC Electrode Microstructure by Electron Tomography and 3D FIB/SEM

Scanning and transmission electron microscopies (SEM and TEM) are two imaging techniques widely used in the study of PEMFC electrode microstructure. They have played a significant role in the improvement of electrode components and the understanding of their main degradation mechanisms. However, the currently available description of the electrode microstructure does not allow yet to unambiguously identify the key microstructural factors that limit gas and proton transport within the electrode. These factors are crucial as they control the performance of the PEMFC, especially at high current density, and are therefore the subject of numerous studies, using either fine electrochemical measurements or modeling approaches.The objective of our work is to provide an improved description of the electrode microstructure by giving quantitative information on its porosity at different scales as well as on the ionomer distribution. For this purpose, we develop 3D characterization techniques applied to the PEMFC electrodes such as electron tomography and 3D FIB/SEM.The studied cathode electrode was prepared using TEC10E50E catalyst from Tanaka that is 50 wt % loading of Pt deposited on high-specific area carbon (HSAC), and Nafion D2020. The Pt loading of the electrode is 0.2 mgPtcm-2 and the ionomer/carbon ratio is 0.8.As a first step, electron tomography analyses were performed on the Pt/HSAC powder to determine the distribution of Pt nanoparticles (NPs) which can be located either on the carbon surface (outer NPs) or inside the carbon porosity (inner NPs)[1-3]. Our strategy is to record tilt image series by using two annular detectors: i) a high angle annular detector avoiding Pt NP diffraction contrast for the 3D image reconstruction of Pt NPs (without diffraction artifact) and ii) a lower angle annular detector that enhances carbon contrast for the 3D image reconstruction of the carbon phase. These two segmented images were then combined (Figure 1), allowing the calculation of the NP size histogram but also of the volume and the surface area of the two NP populations (inner and outer NPs).On the second step, the porosity of the electrode was analyzed by 3D FIB/SEM. A volume of around 10 X 10 x 5 μm3 was imaged with a cubic voxel size of 5 × 5 × 5 nm3. The solid phase (carbon, Pt NPs and ionomer) segmentation was based on a composite image created from two electron detectors (secondary and backscattered electrons) and using a machine learning software. Quantitative data such as the total porosity, the pore size distribution or the agglomerate size distribution of the solid phase were extracted from this volume. Figure 2a shows a 2D slice of this segmented volume where the solid phase is in black and the porosity in white.In order to analyze the electrode porosity with a higher spatial resolution, electron tomography experiment was also performed on a 150 nm thick slice of the electrode cut by ultramicrotomy after being embedded in an epoxy resin. The tilt image series were recorded with a pixel size of 0.5 nm and a field of view of 2 x 2 µm2. The 2D slice of the reconstructed volume (Figure 2b) shows that the largest solid agglomerates detected on the 3D FIB/SEM image can be identified as an agglomeration of numerous carbon particles. The challenge now is to quantify the porosity created between these carbon particles and to determine how many Pt NPs are located in these large agglomerates.Finally, the distribution of the ionomer was analyzed within the electrode similarly to [4]. For this analysis, a 150 nm slice of the electrode was cut by cryo-ultramicrotomy without embedding in epoxy resin as described in [5]. TEM images show that the ionomer is not homogeneously distributed on the surface of the entire carbon particles but is often observed in the concave surface created between the carbon particles (Figure 3). These observations suggest a ionomer distribution in agreement with the heterogeneous ionomer coating model proposed by Inoue et al. [6]. We also suggest that a part of the ionomer could be localized in the small pores located inside the large carbon agglomerates described in the previous paragraph. This could explain why the ionomer is so difficult to detect on TEM images. This work has been done in the frame of the FURTHER-FC project. This project has received funding from the Fuel Cells and Hydrogen 2 Joint Undertaking (now Clean Hydrogen Partnership) under Grant Agreement No 875025. This Joint Undertaking receives support from the European Union’s Horizon 2020 Research and Innovation program, Hydrogen Europe and Hydrogen Europe Research. Figure 1

A deep learning approach to the automatic detection of alignment errors in cryo-electron tomographic reconstructions

Electron tomography is an imaging technique that allows for the elucidation of three-dimensional structural information of biological specimens in a very general context, including cellular in situ observations. The approach starts by collecting a set of images at different projection directions by tilting the specimen stage inside the microscope. Therefore, a crucial preliminary step is to precisely define the acquisition geometry by aligning all the tilt images to a common reference. Errors introduced in this step will lead to the appearance of artifacts in the tomographic reconstruction, rendering them unsuitable for the sample study. Focusing on fiducial-based acquisition strategies, this work proposes a deep-learning algorithm to detect misalignment artifacts in tomographic reconstructions by analyzing the characteristics of these fiducial markers in the tomogram. In addition, we propose an algorithm designed to detect fiducial markers in the tomogram with which to feed the classification algorithm in case the alignment algorithm does not provide the location of the markers. This open-source software is available as part of the Xmipp software package inside of the Scipion framework, and also through the command-line in the standalone version of Xmipp.

Reading recognition of pointer meters based on an improved UNet++ network

Pointer meters are widely used in modern industries, such as petrochemical applications, substations, and nuclear power plants. To overcome the reading errors and inaccurate measurements due to uneven or fluctuating illumination in practical applications, this paper proposes an improved UNet++ network for recognizing pointer meter readings. First, the scale invariant feature transform feature-matching algorithm is used to adjust the captured tilted meter images to a symmetrical and upright shape. Then, the UNet++ network is used to segment the scale and pointer regions in the dashboard to eliminate background interference. Furthermore, part of the convolution in the UNet++ network is replaced with dilated convolution with different expansion rates to expand the perceptual field during network training. In the UNet++ network jump connection, the attention mechanism module is also introduced in the path to enhance the region’s features to be segmented and suppress the parts of the non-segmented area. A hybrid loss function is used for the network model training to prevent the imbalance of the segmented region share. Finally, the distance method is used to read the gauge representation. Experiments were conducted to compare the performance of the proposed method with that of the original UNet++ network in terms of feasibility and precision. The experimental results showed that the recognition reading accuracy was significantly improved by the enhanced network, with the accuracy, sensitivity, and specificity reaching 98.65%, 84.33%, and 99.38%, respectively. Furthermore, when using the improved UNet++ network for numerical reading, the average relative error was only 0.122%, indicating its robustness in a natural environment.

An Ultra-Fast Automatic License Plate Recognition Approach for Unconstrained Scenarios

Recently, with the development of deep learning, Automatic License Plate Recognition (ALPR) has made great progress, However, there are still many challenges to accomplish license plate (LP) recognition under various traffic scenarios. One of them is the detection speed and recognition speed, and the other is the difficulty to recognize the low resolution and highly tilted LP images. In this paper, we present a two-stage ALPR framework to achieve efficient LP detection and recognition in unconstrained scenarios. Our LP detector is based on improved Yolov3-tiny, and we also propose a lightweight recognition network MRNet based on multi-scale features. In order to improve the inference speed, we abandoned the rectification of LP images, and RNNs that are difficult to compute in parallel. Additionally, we also propose a license plate data augmentation method, which achieves more effective augmentation and improves the generalization ability of the network through secondary random and hyperparametric search. As an additional contribution, we provide a challenging dataset collected from real-world driving recorders. The dataset is for multiple LPs in a single image, which makes up for the lack of public datasets in multiple LP scenarios. We evaluated the results on five datasets and showed that we achieved the best performance on almost all test sets, achieving 99.8% accuracy on CCPD with more than 180,000 license plate test sets. In terms of speed, the inference speed for detecting license plates reaches 751 FPS, and the fastest inference time for recognizing a single license plate takes only 2.9 ms.

The influence of scene tilt on saccade directions is amplitude dependent

When exploring a visual scene, humans make more saccades in the horizontal direction than any other direction. While many have shown that the horizontal saccade bias rotates in response to scene tilt, it is unclear whether this effect depends on saccade amplitude. We addressed this question by examining the effect of image tilt on the saccade direction distributions recorded during freely viewing natural scenes. Participants (n = 20) viewed scenes tilted at −30°, 0°, and 30°. Saccade distributions during free viewing rotated by an angle of 12.1° ± 6.7° (t(19) = 8.04, p < 0.001) in the direction of the image tilt. When we partitioned the saccades according to their amplitude we found that small amplitude saccades occurred most in the horizontal direction while large amplitude saccades were more oriented to the scene tilt (p < 0.001). To further study the characteristics of small saccades and how they are affected by scene tilt, we looked at the effect of image tilt on small fixational saccades made while fixating a central target amidst a larger scene and found that fixational saccade distributions did not rotate with scene tilt (−0.3° ±1.7° degrees; t(19) = −0.8, p = 0.39). These results suggest a combined effect of two reference frames in saccade generation: one egocentric reference frame that dominates for small saccades, biases them horizontally, and may be common for different tasks, and another allocentric reference frame that biases larger saccades along the orientation of an image during free viewing.

Fine Classification of UAV Urban Nighttime Light Images Based on Object-Oriented Approach.

Fine classification of urban nighttime lighting is a key prerequisite step for small-scale nighttime urban research. In order to fill the gap of high-resolution urban nighttime light image classification and recognition research, this paper is based on a small rotary-wing UAV platform, taking the nighttime static monocular tilted light images of communities near Meixi Lake in Changsha City as research data. Using an object-oriented classification method to fully extract the spectral, textural and geometric features of urban nighttime lights, we build four types of classification models based on random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN) and decision tree (DT), respectively, to finely extract five types of nighttime lights: window light, neon light, road reflective light, building reflective light and background. The main conclusions are as follows: (i) The equal division of the image into three regions according to the visual direction can alleviate the variable scale problem of monocular tilted images, and the multiresolution segmentation results combined with Canny edge detection are more suitable for urban nighttime lighting images; (ii) RF has the highest classification accuracy among the four classification algorithms, with an overall classification accuracy of 95.36% and a kappa coefficient of 0.9381 in the far view region, followed by SVM, KNN and DT as the worst; (iii) Among the fine classification results of urban light types, window light and background have the highest classification accuracy, with both UA and PA above 93% in the RF classification model, while road reflective light has the lowest accuracy; (iv) Among the selected classification features, the spectral features have the highest contribution rates, which are above 59% in all three regions, followed by the textural features and the geometric features with the smallest contribution rates. This paper demonstrates the feasibility of nighttime UAV static monocular tilt image data for fine classification of urban light types based on an object-oriented classification approach, provides data and technical support for small-scale urban nighttime research such as community building identification and nighttime human activity perception.

Deep Rotation Correction Without Angle Prior.

Not everybody can be equipped with professional photography skills and sufficient shooting time, and there can be some tilts in the captured images occasionally. In this paper, we propose a new and practical task, named Rotation Correction, to automatically correct the tilt with high content fidelity in the condition that the rotated angle is unknown. This task can be easily integrated into image editing applications, allowing users to correct the rotated images without any manual operations. To this end, we leverage a neural network to predict the optical flows that can warp the tilted images to be perceptually horizontal. Nevertheless, the pixel-wise optical flow estimation from a single image is severely unstable, especially in large-angle tilted images. To enhance its robustness, we propose a simple but effective prediction strategy to form a robust elastic warp. Particularly, we first regress the mesh deformation that can be transformed into robust initial optical flows. Then we estimate residual optical flows to facilitate our network the flexibility of pixel-wise deformation, further correcting the details of the tilted images. To establish an evaluation benchmark and train the learning framework, a comprehensive rotation correction dataset is presented with a large diversity in scenes and rotated angles. Extensive experiments demonstrate that even in the absence of the angle prior, our algorithm can outperform other state-of-the-art solutions requiring this prior. The code and dataset are available at https://github.com/nie-lang/RotationCorrection.

Research on the Recognition Method of the Axle End Mark of a Train Wheelset Based on Machine Vision

Whether the wheelset of a high-speed train has defects such as cracks is very important to the safety of high-speed trains. Hence, the wheelset must be regularly inspected for flaws. For flaw detection of a wheelset, it is necessary to record the axle end information of the wheelset to correlate with the flaw detection results. To quickly and accurately identify the axle end mark of the wheelset, an automatic identification method based on machine vision is proposed. Our method identifies seven types of marks on the axle end, including the smelting number, steel grade number, unit number, sequence number, year and month, axle type mark, and the azimuth mark. Using the established automatic identification method of axle end marks, based on Retinex theory, an improved dual-core Laplacian combined with Gaussian filtering operation is proposed to solve the problem of the low contrast of the wheelset axle end image. An improved image tilt correction algorithm based on the combination of Hough circle detection and bilinear interpolation is proposed, which solves the angle tilt problem of the target character area of the axis end image. To handle the various types of axis end markers and the small amount of data, a retraining method to improve recognition accuracy is proposed. This method first uses Chi_Sim as the basic font for training and then retrains based on the trained font. Finally, Tesseract-OCR is used to improve the accuracy of the recognition results. Experiments are carried out by developing an automatic recognition program for axle end marks. The results show that the proposed method can effectively identify and classify seven-character types, and the recognition accuracy reaches 96.88% while the recognition time of each image is 5.88 s.

Cryogenic electron microscopy approaches that combine images and tilt series.

Cryogenic electron microscopy can be widely applied to biological specimens from the molecular to the cellular scale. In single-particle analysis, 3D structures may be obtained in high resolution by averaging 2D images of single particles in random orientations. For pleomorphic specimens, structures may be obtained by recording the tilt series of a single example of the specimen and calculating tomograms. Where many copies of a single structure such as a protein or nucleic acid assembly are present within the tomogram, averaging of the sub-volumes (subtomogram averaging) has been successfully applied. The choice of data collection method for any given specimen may depend on the structural question of interest and is determined by the radiation sensitivity of the specimen. Here, we survey some recent developments on the use of hybrid methods for recording and analysing data from radiation-sensitive biological specimens. These include single-particle reconstruction from 2D images where additional views are recorded at a single tilt angle of the specimen and methods where image tilt series, initially used for tomogram reconstruction, are processed as individual single-particle images. There is a continuum of approaches now available to maximize structural information obtained from the specimen.

An Effective Method for the Recognition and Verification of Bangladeshi Vehicle Digital Number Plates

In this study, an effective and automatic technique has been proposed to detect, recognize and verify Bangladeshi number plates. There are four steps in the proposed method: Pre-processing, extraction and segmentation, recognition and verification of the number plate. Image contrast-enhancing and tilt correction techniques are used in the proposed method. Different techniques like edge detection, morphological operations, bounding box method and connected component analysis are used for the extraction and segmentation of number plates and their characters. The template matching method is used for recognizing the characters. The authentication and verification process for a vehicle is the distinctive feature of the proposed method. We have developed a dataset of 500 Bangladeshi number plate images for our research. A rich template dataset and a cloud database containing vehicle details have also been developed for number plate character recognition and vehicle verification purposes, respectively. The proposed method achieves detection, extraction, segmentation and recognition accuracies of 96.8, 94.8, 98.3 and 97.6%, respectively. It is observed that the proposed technique outperformed many other existing techniques.

TomoAlign: A novel approach to correcting sample motion and 3D CTF in CryoET

TomoAlign is a software package that integrates tools to mitigate two important resolution limiting factors in cryoET, namely the beam-induced sample motion and the contrast transfer function (CTF) of the microscope. The package is especially focused on cryoET of thick specimens where fiducial markers are required for accurate tilt-series alignment and sample motion estimation. TomoAlign models the beam-induced sample motion undergone during the tilt-series acquisition. The motion models are used to produce motion-corrected subtilt-series centered on the particles of interest. In addition, the defocus of each particle at each tilt image is determined and can be corrected, resulting in motion-corrected and CTF-corrected subtilt-series from which the subtomograms can be computed. Alternatively, the CTF information can be passed on so that CTF correction can be carried out entirely within external packages like Relion. TomoAlign serves as a versatile tool that can streamline the cryoET workflow from initial alignment of tilt-series to final subtomogram averaging during in situ structure determination.

Generalized chromatic aberrations in non-rotationally symmetric optical systems–Part I: mathematical approach

In non-rotationally symmetric optical systems, chromatic aberrations must be defined in a generalized way with reference to the optical axis ray and optimal tilted parabasal image plane of the central wavelength. In this paper, the definition of generalized chromatic aberrations is clarified. The mathematical calculation in optical systems with arbitrary symmetry and surface shape is realized by ray- and wavefront-based methods, respectively, that are originally identical. The additivity of wavefront after each surface ensures the surface decomposition of chromatic aberrations. In the end, the influence of pupil aberration, which is of a higher order, is discussed. The consistency of our methods with Seidel aberrations in the lowest aberration order of the rotationally symmetric system and the application of our methods in diverse non-rotationally symmetric refractive systems will be addressed in detail in Part II.