Sub-pixel Accuracy Research Articles

Postrecording Pixel-Reconstruction Approach for Correcting the Lateral Drifts in Surface Plasmon Resonance Microscope.

Surface plasmon resonance microscope (SPRM) sample stage inevitably suffers from lateral drifts as a result of many environmental factors including thermal fluctuation, mechanical vibration, and relaxation. It places great obstacles to time-lapsed imaging and measurements that need high spatial resolution or long recording time. Existing solutions often require experimental efforts such as the addition of optical markers together with piezoelectric stage-based active feedback configurations. Herein, we propose an all-digital, postrecording image-processing method to remove the lateral drift in a series of time-lapsed SPRM images. The method first calculates the value of lateral drift at subpixel accuracy by combining image cross-correlation analysis and superlocalization strategy. It subsequently reconstructed the drift-free image sequences in a pixel-by-pixel and frame-by-frame manner, according to the linear decomposition and reconstruction principle. This method purely relies on image processing, and it does not require any experimental efforts or hardware. In addition to SPRM, we further demonstrated the applicability of the present method in other types of optical imaging techniques including bright-field transmission microscope and dark-field scattering microscope.

PHOTOGRAMMETRIC TECHNOLOGY FOR REMOTE HIGH-PRECISION 3D MONITORING OF CRACKS AND DEFORMATION JOINTS OF BUILDINGS AND CONSTRUCTIONS

Abstract. Monitoring of cracks and deformation joints of buildings and engineering constructions can be performed effectively using contemporary methods of photogrammetry. Our study allowed us to design the technology for such a monitoring. This technology is adapted for use by building operation and building inspection specialists and does not require special knowledge in photogrammetry. The monitoring equipment includes two blocks of photogrammetric deformation marks, a digital camera and processing software. Each block of deformation marks is designed as a plate of 60 by 40 mm size where several dozens of marks are fixed (size of the plate and number of marks may vary). The relative positions of the marks on the plate are determined while block calibration with an accuracy of several microns. While monitoring is performed, two blocks of deformation marks are fixed on both sides of the crack or deformation join. Then marks are photographed. Almost any digital camera is suitable, beginning with smartphone camera and ending with specialized photogrammetric camera. Further processing of collected imagery is performed on the basis of rigorous methods of photogrammetry (specialized software were developed). The processing assumes automatic identification and measurement of marks on digital photographic images with sub-pixel accuracy. Additionally, the photogrammetric calibration and distortion correction are performed for each image. Three-dimensional spatial solution is possible both in the case of single image processing, and in the case of stereopair processing. The dynamics of crack development in three dimensions is determined by the results of several cycles of observations collected over period. Our technology allows to ensure the accuracy of the coordinates and deformations at the level of 0.005–0.020 mm for the photographing distances from 0.1 to 40 m.

H-EM: An algorithm for simultaneous cell diameter and intensity quantification in low-resolution imaging cytometry

Fluorescent cytometry refers to the quantification of cell physical properties and surface biomarkers using fluorescently-tagged antibodies. The generally preferred techniques to perform such measurements are flow cytometry, which performs rapid single cell analysis by flowing cells one-by-one through a channel, and microscopy, which eliminates the complexity of the flow channel, offering multi-cell analysis at a lesser throughput. Low-magnification image-based cytometers, also called “cell astronomy” systems, hold promise of simultaneously achieving both instrumental simplicity and high throughput. In this magnification regime, a single cell is mapped to a handful of pixels in the image. While very attractive, this idea has, so far, not been proven to yield quantitative results of cell-labeling, mainly due to the poor signal-to-noise ratio present in those images and to partial volume effects. In this work we present a cell astronomy system that, when coupled with custom-developed algorithms, is able to quantify cell intensities and diameters reliably. We showcase the system using calibrated MESF beads and fluorescently stained leukocytes, achieving good population identification in both cases. The main contribution of the proposed system is in the development of a novel algorithm, H-EM, that enables inter-cluster separation at a very low magnification regime (2x). Such algorithm provides more accurate brightness estimates than DAOSTORM when compared to manual analysis, while fitting cell location, brightness, diameter, and background level concurrently. The algorithm first performs Fisher discriminant analysis to detect bright spots. From each spot an expectation-maximization algorithm is initialized over a heterogeneous mixture model (H-EM), this algorithm recovers both the cell fluorescence and diameter with sub-pixel accuracy while discriminating the background noise. Finally, a recursive splitting procedure is applied to discern individual cells in cell clusters.

Deep learning based topology guaranteed surface and MME segmentation of multiple sclerosis subjects from retinal OCT.

Optical coherence tomography (OCT) is a noninvasive imaging modality that can be used to obtain depth images of the retina. Patients with multiple sclerosis (MS) have thinning retinal nerve fiber and ganglion cell layers, and approximately 5% of MS patients will develop microcystic macular edema (MME) within the retina. Segmentation of both the retinal layers and MME can provide important information to help monitor MS progression. Graph-based segmentation with machine learning preprocessing is the leading method for retinal layer segmentation, providing accurate surface delineations with the correct topological ordering. However, graph methods are time-consuming and they do not optimally incorporate joint MME segmentation. This paper presents a deep network that extracts continuous, smooth, and topology-guaranteed surfaces and MMEs. The network learns shape priors automatically during training rather than being hard-coded as in graph methods. In this new approach, retinal surfaces and MMEs are segmented together with two cascaded deep networks in a single feed forward propagation. The proposed framework obtains retinal surfaces (separating the layers) with sub-pixel surface accuracy comparable to the best existing graph methods and MMEs with better accuracy than the state-of-the-art method. The full segmentation operation takes only ten seconds for a 3D volume.

A Novel 3D-Printed Patient Positioning Platform to Accommodate Curved Couch of MR Scanner Optimizes Precision Treatment Planning Workflow for Abdominal Radiotherapy.

Magnetic Resonance Imaging (MR) is increasingly used in treatment planning (TP) owing to superior soft tissue contrast versus computed tomography (CT). Organ deformation in patient positioning on a curved MR couch versus a flat CT couch causes registration difficulties. This may lead to contour errors and tumor volume misjudgment, particularly in organs poorly visualized on CT (i.e. liver). MR-based simulation systems (MR-BSS) address this issue but are not widely available. In this study, we fabricated a 3D-printed platform (3D-PP) adapted to the MR couch and body array to enable scans in the TP position and optimize MR-guided TP in the absence of a dedicated MR-BSS. We demonstrate that a 3D-PP is low cost, does not interfere with image quality, and is easily implemented by radiology staff. The 3D-PP was drafted with computer-aided design to fit the table arc and 12 channel body array coil of a 1.5 T, 70 cm wide bore, MR scanner. The 3D-PP creates a flat surface to position an immobilization vacuum bag typically used in treatment. The 3D-PP consists of ¼” thick deck and 3/32” thick alignment wings mounted on ten risers. The deck and alignment wings are polyoxymethylene homopolymer laser-cut with a contact-free CO2 beam. Risers are fabricated from polylactide in a thermoplastic 3D printer. A urethane immobilization vacuum cushion (88 x 55 cm, 15 L fill) was fabricated for an 8-channel upper annular MR phantom. CT was acquired in an 85 cm bore simulation CT (120kVp, 512x512 in-plane dimensions, 0.11cm x 0.11cm pixel size, 2mm thickness). Standard body MR sequences were done including single shot fast spin echo (SS-FSE), 3D fast spoiled gradient echo with fat suppression(FSPGR FS), T2 fat suppression(T2 FS), balanced steady-state free precession (BSSFP), dual echo fast spin echo(DE FSE), and diffusion weight imaging (DWI). MR and CT scans were exported to a treatment planning system and fused using a local correlation algorithm capable of both rigid and deformable registration. An anthropomorphic phantom and quality control devices were employed for MR scans. SS-FSE, FSPGR, FSPGR FS, T2 FS, BSSFP, DEFSE, and DWI were analyzed to determine the effect of any artifacts contributed by the 3D-PP. Neither appreciable signal loss nor artifact were observed on any scan. Each MR-CT fusion required negligible computation time (< 2 seconds). In-plane registration was within sub-pixel accuracy for every slice in each sequence. We report a new, low cost approach to optimize MR-guided TP. A 3D-PP enables MR scans in the simulated TP position without appreciable loss of image quality or interruption of radiology workflow. A 3D-PP in a 70 cm wide bore MR system accommodates most treatment positions, including wing board. We are implementing this process into our abdominal SBRT workflow. The 3D-PP design and fabrication can be modified for the workflow of most clinics.

Photogrammetric Solution for Analysis of Out-Of-Plane Movements of a Masonry Structure in a Large-Scale Laboratory Experiment

This paper proposes a photogrammetric procedure able to determine out-of-plane movements experienced by a masonry structure subjected to a quasi-static cyclic test. The method tracks the movement of circular targets by means of a coarse-to-fine strategy. These targets were captured by means of a photogrammetric network, made up of four cameras optimized following the precepts of a zero-, first-, and second-order design. The centroid of each circular target was accurately detected for each image using the Hough transform, a sub-pixel edge detector based on the partial area effect, and a non-linear square optimization strategy. The three-dimensional (3D) coordinates of these targets were then computed through a photogrammetric bundle adjustment considering a self-calibration model of the camera. To validate the photogrammetric method, measurements were carried out in parallel to an ongoing test on a full-scale two-story unreinforced masonry structure (5.4 × 5.2 × 5.4-m) monitored with more than 200 contact sensors. The results provided by the contact sensors during one of the load phases were compared with those obtained by the proposed approach. According to this accuracy assessment, the method was able to determine the out-of-plane displacement during the quasi-static cyclic test with a sub-pixel accuracy of 0.58.

Precise fabrication of large-area microstructures by digital oblique scanning lithography strategy and stage self-calibration technique

We proposed an efficient and low-cost method for precise fabrication of large-area microstructures. A programmable digital lithography system based on digital micromirror device (DMD) oblique scanning strategy was developed, which can effectively reduce pixel quantization errors and complete a sub-pixel exposure accuracy. To further reduce the cost and enhance the flexibility of the fabrication system, a two-dimensional (2D) stage self-calibration technique is studied, which can replace the laser interferometer measurement (LIM) system and ensure the calibration accuracy well. The presented method is promising for developing large-area microstructure applications such as flat panel display (FPD), organic light-emitting diodes (OLED).

Urban thermal micro-mapping using satellite imagery and ground-truth measurements: Kyiv city area case study

The aim of this research is to enhance approaches existing for the assessment of cities thermal conditions under climate change impact by using multispectral satellite data for Kyiv city area. This paper describes the method and results of the Earth’s surface temperature (LST) and thermal emissivity calculation. Particularly, the thermal distribution was estimated based on spectral densities according to Planck’s law for “grey bodies” by using the Landsat-8 TIRS and Sentinel-2 MSI satellite imagery. Furthermore, the result was calibrated by ground data collected during the ground-truth measurements of the typical city surfaces temperature and thermal emissivity. The spatial resolution of the LST images obtained was enhanced by using the approach of subpixel processing, that is the pairs of invariant images shifted with subpixel accuracy. As a result, such an approach allowed to enhance the spatial resolution of the image up 46%, which is much higher than the potential performance of the thermal imaging sensors existing. The interrelation between the Earth’s surface type and the temperature was revealed by the results of the Sentinel-2A MSI image of 21 August 2017 supervised classification. Thus, the image was divided into the six major classes of the urban environment: building’s rooftops, roads surface, bare soil, grass, wood, and water. As a result, surfaces with vegetation much more cool next to artificial ones. The time-series analysis of 18 thermal images (Landsat TM and Landsat-8 TIRS) of Kyiv for the period from 6 Jun 1985 till 1 June 2018 was done for spatiotemporal changes investigation. Therefore, the sites of the LST thermal anomalies caused by landscape changes were developed. Among them are the sites of increased LST where thw “Olimpiyskiy” national sport center and adjacent parking was built and the site of decreased LST where the tram depot was liquidated and the territory was flooded.

The Extension of Phase Correlation to Image Perspective Distortions Based on Particle Swarm Optimization.

Phase correlation is one of the widely used image registration method in medical image processing and remote sensing. One of the main limitations of the phase correlation-based registration method is that it can only cope with Euclidean transformations, such as translation, rotation and scale, which constrain its application in wider fields, such as multi-view image matching, image-based navigation, etc. In this paper, we extended the phase correlation to perspective transformation by the combination of particle swarm optimization. Inspired by optic lens alignment based on interference, we propose to use the quality of PC fringes as the similarity, and then the aim of registration is to search for the optimized geometric transformation operator, which obtain the maximize value of PC-based similarity function through particle swarm optimization approach. The proposed method is validated by image registration experiments using simulated terrain shading, texture and natural landscape images containing different challenges, including illumination variation, lack of texture, motion blur, occlusion and geometric distortions. Further, image-based navigation experiments are carried out to demonstrate that the proposed method is able to correctly recover the trajectory of camera using multimodal target and reference image. Even under great radiometric and geometric distortions, the proposed method is able to achieve 0.1 sub-pixel matching accuracy on average while other methods fail to find the correspondence.

Characterizing beach changes using high-frequency Sentinel-2 derived shorelines on the Valencian coast (Spanish Mediterranean).

Shoreline position can be efficiently extracted with subpixel accuracy from mid-resolution satellite imagery using tools as SHOREX. However, it is necessary to develop procedures for deriving descriptors of the beach morphology and its changes in order to become truly useful data for characterizing the coastal dynamism. A new approach is proposed based on a spatiotemporal model of the beach widths. Divided into 80m analysis segments, it offers a robust and detailed characterization of the beach state along large micro-tidal regions, with continuous information through time and space. Geographical and temporal differences can be recognized and measured, making it possible to study the beach response both to general factors (as wave conditions) and to punctual anthropic actions (as small sand nourishments). Widths were defined throughout two and a half years from 60 shorelines (3.04m RMSE) covering 50km of the Gulf of Valencia. Important width contrasts appeared along the study site associated with sediment imbalances motivated by sediment traps and other anthropic actions. Segments too narrow for maintaining the recreational function were located and mapped (16% narrower than 30m). Short-term width changes appeared linked to storm events, with fast retreatments and slow recoveries. Punctually, even small-magnitude nourishments created perceptible changes in width (12,830m3 were associated with a 4m increase). This novel description of the beach state and its changes from Satellite-Derived Shorelines is useful for coastal management, especially considering the global coverage of these free satellite images. It may improve the comprehension of coastal processes as well as monitor human interventions on the coast, helping in the decision-making.

Top Cloud Motion Field of Typhoon Megi-2016 Revealed by GF-4 Images

Gaofen-4 (GF-4) is the first high-resolution geostationary satellite of China, launched on December 29, 2015. Its visible-near infrared optical sensor is capable of imaging the earth at 50 m resolution, covering a 512 km $\times512$ km area with a minimum imaging interval of 20 s. More than 300 GF-4 images, taken in four sequences with imaging rates of 36 and 69 s per scene, captured the development of Typhoon Megi-2016—from its peak in the afternoon of September 26, 2016 to its dispersion two days later on September 28. These consecutive images recorded the motion field of the typhoon’s top clouds nearly continuously. By using advanced image matching technology, the motion has been estimated for every pixel, at subpixel accuracy from image pairs with 179 and 206 s intervals. The process has generated time series of atmospheric motion vector (AMV) fields at 50 m spatial resolution for the whole imaged area. It is the first time, to our knowledge, that such high-resolution and nearly continuous AMV data of a typhoon system have been produced. The data provide accurate measurements of the typhoon top cloud motion speed and direction, and reveal quantitative details of the motion field spatiotemporal evolution at high altitude. The finding that the high altitude top cloud motion speed is significantly lower than that recorded at low altitude below the planetary boundary layer is of scientific value and further exploitation of the motion data can lead to a better understanding of typhoon dynamics and thus improve cyclone modeling.

A Calculation Method for Dressing Amount of Monolayer Electroplated Grinding Wheel Based on Zernike Moment

Aiming at the difficulties in accurate prediction of the dressing amount of monolayer electroplated grinding wheel, a new calculation method based on image processing was proposed. For the purpose of increasing the measurement precision, all images were captured by CCD camera and a sub-pixel level accuracy edge detection approach based on Zernike moment was used. Two dressing amount of electroplated grinding wheel were calculated by protrusion height and run-out respectively, and the larger number of them was chosen as the optimal dressing amount. Finally, this method is applied to calculate the optimal dressing amount of 30 gothic-arc profile electroplated grinding wheels, and they were dressed by optical contour grinder. The results showed that, form precision of grinding wheels and roughness of workpiece were fine.

An automatic method for precise 3D registration of high resolution satellite images and Airborne LiDAR Data

ABSTRACTPrecise 3D registration of Light Detection and Ranging (LiDAR) data and High-Resolution Satellite Image (HRSI) is the prerequisite in the many remote sensing applications. An automatic registration process involves two main steps including the detection of corresponding entities and the estimation of a relating mathematical model. Typical matching techniques, which are generally developed to match consistent data sources, are prone to fail in case of matching between heterogeneous data sources (e.g. LiDAR and optical images). This paper proposes a three-step method to give in hand an accurate and automatic 3D registration technique between LiDAR data and the HRSI. The first step introduces a new product called Optical Consistent LiDAR Product (OCLP) which is meant to be consistent with HRSI from the radiometric point of view. The OCLP is generated using raster maps of the LiDAR heights and intensities along with information of the sun position at the acquisition time of HRSI. This new product proved to be very promising as a matching entity with HRSI. In the second step, a 3D model is estimated robustly through the matched points identified by the well-known Scale Invariant Feature Transform (SIFT) technique between the OCLP and the HRSI. The last step of the proposed method aims to strengthen this 3D model which is accomplished via iterative closest edge points matching technique. To do so, the coarse 3D model is iteratively improved based on the matching results obtained on the image edges. The proposed method was implemented on 20 different datasets containing various urban textures. The results indicate the effectiveness of the proposed method since in some cases it could achieve to sub-pixel accuracies which are the utmost expectation for a registration technique.

Registration Algorithm Based on Line-Intersection-Line for Satellite Remote Sensing Images of Urban Areas

Image registration is an important step in remote sensing image processing, especially for images of urban areas, which are often used for urban planning, environmental assessment, and change detection. Urban areas have many artificial objects whose contours and edges provide abundant line features. However, the locations of line endpoints are greatly affected by large background variations. Considering that line intersections remain relatively stable and have high positioning accuracy even with large background variations, this paper proposes a high-accuracy remote sensing image registration algorithm that is based on the line-intersection-line (LIL) structure, with two line segments and their intersection. A double-rectangular local descriptor and a spatial relationship-based outlier removal strategy are designed on the basis of the LIL structure. First, the LILs are extracted based on multi-scale line segments. Second, LIL local descriptors are built with pixel gradients in the LIL neighborhood to realize initial matching. Third, the spatial relations between initial matches are described with the LIL structure and simple affine properties. Finally, the graph-based LIL outlier removal strategy is conducted and incorrect matches are eliminated step by step. The proposed algorithm is tested on simulated and real images and compared with state-of-the-art methods. The experiments prove that the proposed algorithm can achieve sub-pixel registration accuracy, high precision, and robust performance even with significant background variations.

The height Digital Image Correlation (hDIC) technique for the identification of triaxial surface deformations

This paper introduces the height digital image correlation (hDIC) technique for the identification of triaxial deformations. Conventional digital image correlation (DIC) uses pixel image intensity as the basis for the determination of in-plane displacement fields. We demonstrate the advantages of using the out-of-plane surface height and its variation during deformation to extract information about triaxial (in-plane and out-of-plane) displacement fields. Surface height maps can be obtained by optical profilometry or scanning probe microscopies, e.g. stylus profilometry, coordinate measurement machine (CMM), or atomic force microscope (AFM). The changes in height during deformation are sufficiently small to allow efficient correlation of in-plane displacements with sub-pixel accuracy, yet also provide information about out-of-plane displacements. In the present study, the contour map of surface height was created using digital dynamic focus (“deep focus”) optical microscope. The correlation between the reference and target maps to extract the displacement data was accomplished by a two-step correlation process. Initially, triaxial Cartesian coordinate data of reference and target data sets were cross-correlated at integer-pixel level sensitivity. This was followed by sub-pixel correlation using gradient descent method. As an example of technique application, Al alloy test specimens were subjected to large tensile deformations to failure. Good agreement was found between height digital image correlation (hDIC) analysis of displacements and strains and the reference material properties, with the evolution of both in-plane strains and out-of-plane displacements showing progressive localisation during post-critical deformation beyond the sample's ultimate tensile strength (UTS).

Stereo rectification of pushbroom satellite images by robustly estimating the fundamental matrix

ABSTRACT Stereo rectification is one of the most important steps for stereo matching and subsequently for digital surface model generation from satellite stereo images. This study proposes a new framework to rectify two pushbroom images along the epipolar geometry in order to omit the vertical parallax between two images. Here, we assume the interior and relative parameters between the two pushbroom images are not known and the images can be taken at different dates. Traditional stereo rectification methods of pushbroom images require metadata such as rational polynomial coefficients (RPCs), parameters of physical sensor model or ground control points (GCPs). In this study, we develop an image-based framework for stereo rectification, which works without the need for such data. In the proposed framework, the correspondences are densely extracted by a tilling strategy, and then the fundamental matrix is robustly estimated by two geometric constraints. Both affine and projective fundamental matrices could be used for stereo rectification from pushbroom stereo images. The results on IRS P5, World view III, GeoEye and IKONOS stereo pairs as well as on multi-date stereo images demonstrate that the pushbroom images are rectified with sub-pixel accuracy.

Study on Effect of Tool Nose Radius Wear on Hybrid Roughness Parameters during Turning Using Vision-Based Approach

In the past, most researchers have investigated the effect of tool flank wear on the average roughness parameter (Ra) during turning. The Ra parameter, however, is sensitive only to the height variations of the machined workiece profile and is insensitive to the spatial changes to the profile caused by gradual increase in tool wear. In this work, a non-contact vision-based method was used to investigate the effect of tool nose radius wear on the hybrid surface roughness parameters during finish turning. The roughness profile of the workpiece surface diametrically opposite the cutting tool was captured during turning of AISI 1035 steel using a digital camera. A high shutter speed (1/4000 s) was used to freeze the motion of the rotating spindle and obtain a blur-free image of the workpiece. The edge of the workpiece profile was extracted to sub-pixel accuracy using the grey level invariant moment method. The hybrid surface roughness parameters were determined from the workpiece profile. The effect on increased nose wear area on these parameters were investigated. Among these roughness parameters the mean wavelength (Rλa) parameter showed the best correlation with tool nose radius wear with coefficients of determination of 0.9734.

Multi-object tracking in MRI-guided radiotherapy using the tracking-learning-detection framework

The application of the tracking-learning-detection (TLD) framework; a performant tracking algorithm for real-life objects in CCD video, was evaluated and successfully optimized for tracking anatomical structures in low-quality 2D cine-MRI acquired during MRI-guided radiotherapy. Sub-pixel tracking accuracy and >95% precision and recall was achieved despite significant deformations and periodical disappearance.

Sub-pixel image registration on an embedded Nanosatellite Platform

CubeSats are limited in their physical size, which limits the physical size of payloads they can carry, thereby limiting the image quality of CubeSat Earth Observation missions. Algorithms exist that combine partially overlapping images to produce better output image quality. These algorithms may either improve the signal-to-noise ratio via averaging, increase resolution via super-resolution or merely remove redundant information via mosaicing. Such algorithms typically only function properly if the geometric transformations between consecutive images are known with high accuracy. These algorithms can either be applied terrestrially or on-board a satellite. Downloading large raw image data sets for terrestrial processing is impractical for a CubeSat mission, and therefore an on-board solution is desirable.This paper discusses how to accurately determine the transformation between consecutive images on-board, laying the foundation for efficient on-board de-noising, super-resolution and mosaicing. Two common methods used to determine translation – normalised cross correlation (NCC) and phase correlation – are investigated. From simulated results, NCC is shown to be the better candidate for our application. NCC achieves sub-pixel accuracy by making use of polynomial least squares regression. NCC is well suited for implementation on a satellite platform where images are captured in quick succession, resulting in partially overlapping images with little rotation between frames.We compare two potential hardware platforms – the MicroZed 7020 and Jetson TK1 – and then describe how we implemented our proposed solution onto the former, using a hardware description language. The effect of tuning NCC's parameters – specifically window and template size – is investigated with regards to hardware utilisation and accuracy. Software simulation and firmware-implementation results, using simulated data, are compared and discussed. We conclude by discussing expected full system runtime and energy efficiency.

ElectronDiffraction tools, a DigitalMicrograph package for electron diffraction analysis

Microstructure analysis based on the electron diffraction patterns (ED or SAED) or high-resolution transmission electron microscopy images (HRTEM) has become a major technique for the local structural characterization of materials. Three traditional methods of electron diffraction analysis are pattern indexing, phase identification, and unit cell determination. But for the lack of analysis tools, most of the people usually manually measure the diffraction spots to index the diffraction pattern or measure a few lattice planes (fringes in HRTEM or diffraction rings) to perform phase analysis. For the need of the daily-used diffraction analysis, we present a new processing & analysis package for electron diffraction pattern, ElectronDiffraction tools. The program was written as a package of DigitalMicrograph, a very popular software to record and analyze the data of transmission electron microscopes (TEM). Autocorrelation and digital filter method were implanted in it to yield a high-quality Fourier transform diffractogram from an HRTEM image, Gaussian filter and Gaussian fitting were applied to locate diffraction spots with sub-pixel accuracy. It can automatically measure diffraction spots to index the spotty-like diffraction pattern of the known crystal; intensity profile or surface plot extracted from a diffraction pattern can be used to carry out phase analysis just like ‘Search-Match’ in XRD, and the extracted d-spacing table can be used to determine the unit cell of the unknown crystal. Program summaryProgram Title: ElectronDiffraction toolsProgram Files doi:http://dx.doi.org/10.17632/mfw96cbgp4.1Licensing provisions: GNU Public License GPLv3Programming language: DigitalMicrograph scripting languageNature of problem: It is a time-consuming procedure with a large analysis error for the traditional TEM analysis based on the SAED pattern or HRTEM images.Solution method: Gaussian filtering and Gaussian fitting are implemented in ElectronDiffraction tools to automatically measure diffraction spots for the pattern indexing and the extraction of the d-spacing table. The azimuthal rotation-average projection is applied to extract the intensity profile, linear image and its surface plot for the phase analysis. Moreover, an accurate d-spacing table derived from spots extension can be used to determine the unit cell of the unknown crystal.Additional comments: The program can also be obtained from https://github.com/hlshi527214/ElectronDiffraction-tools/tree/master/Installer%20files