A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster.

Drosophila melanogaster has a rich repertoire of innate and learned behaviors. Its 100,000-neuron brain is a large but tractable target for comprehensive neural circuit mapping. Only electron microscopy (EM) enables complete, unbiased mapping of synaptic connectivity; however, the fly brain is too large for conventional EM. We developed a custom high-throughput EM platform and imaged the entire brain of an adult female fly at synaptic resolution. To validate the dataset, we traced brain-spanning circuitry involving the mushroom body (MB), which has been extensively studied for its role in learning. All inputs to Kenyon cells (KCs), the intrinsic neurons of the MB, were mapped, revealing a previously unknown cell type, postsynaptic partners of KC dendrites, and unexpected clustering of olfactory projection neurons. These reconstructions show that this freely available EM volume supports mapping of brain-spanning circuits, which will significantly accelerate Drosophila neuroscience. VIDEO ABSTRACT.

Lateral response artifact correction method using image stitching technique in radiochromic film dosimetry.

PURPOSE: Lateral response artifact (LRA) is caused by the interaction between film and flatbed scanner in the direction perpendicular to the scanning direction. This can significantly affect the accuracy of patient-specific quality assurance (QA) in cases involving large irradiation fields. We hypothesized that by utilizing the central area of the flatbed scanner, where the magnitude of LRA is relatively small, the LRA could be mitigated effectively. This study proposes a practical solution using the image-stitching technique to correct LRA for patient-specific QA involving large irradiation fields. METHODS: Gafchromic™ EBT4 film and Epson Expression ES-G11000 flatbed scanner were used in this study. The image-stitching algorithm requires a spot between adjacent images to combine them. The film was scanned at three locations on a flatbed scanner, and these images were combined using the image-stitching technique. The combined film dose was then calculated and compared with the treatment planning system (TPS)-calculated dose using gamma analysis (3%/2 mm). Our proposed LRA correction was applied to several films exposed to 18 × 18 cm2 open fields at doses of 200, 400, and 600 cGy, as well as to four clinical Volumetric Modulated Arc Therapy (VMAT) treatment plans involving large fields. RESULTS: For doses of 200, 400, and 600 cGy, the gamma analysis values with and without LRA corrections were 95.7% versus 67.8%, 95.5% versus 66.2%, and 91.8% versus 35.9%, respectively. For the clinical VMAT treatment plan, the average pass rate ± standard deviation in gamma analysis was 94.1% ± 0.4% with LRA corrections and 72.5% ± 1.5% without LRA corrections. CONCLUSIONS: The effectiveness of our proposed LRA correction using the image-stitching technique was demonstrated to significantly improve the accuracy of patient-specific QA for VMAT treatment plans involving large irradiation fields.

Semantic Aware Stitching for Panorama.

The most critical aspect of panorama generation is maintaining local semantic consistency. Objects may be projected from different depths in the captured image. When warping the image to a unified canvas, pixels at the semantic boundaries of the different views are significantly misaligned. We propose two lightweight strategies to address this challenge efficiently. First, the original image is segmented as superpixels rather than regular grids to preserve the structure of each cell. We propose effective cost functions to generate the warp matrix for each superpixel. The warp matrix varies progressively for smooth projection, which contributes to a more faithful reconstruction of object structures. Second, to deal with artifacts introduced by stitching, we use a seam line method tailored to superpixels. The algorithm takes into account the feature similarity of neighborhood superpixels, including color difference, structure and entropy. We also consider the semantic information to avoid semantic misalignment. The optimal solution constrained by the cost functions is obtained under a graph model. The resulting stitched images exhibit improved naturalness. Extensive testing on common panorama stitching datasets is performed on the algorithm. Experimental results show that the proposed algorithm effectively mitigates artifacts, preserves the completeness of semantics and produces panoramic images with a subjective quality that is superior to that of alternative methods.

SC-AOF: A Sliding Camera and Asymmetric Optical-Flow-Based Blending Method for Image Stitching.

Parallax processing and structure preservation have long been important and challenging tasks in image stitching. In this paper, an image stitching method based on sliding camera to eliminate perspective deformation and asymmetric optical flow to solve parallax is proposed. By maintaining the viewpoint of two input images in the mosaic non-overlapping area and creating a virtual camera by interpolation in the overlapping area, the viewpoint is gradually transformed from one to another so as to complete the smooth transition of the two image viewpoints and reduce perspective deformation. Two coarsely aligned warped images are generated with the help of a global projection plane. After that, the optical flow propagation and gradient descent method are used to quickly calculate the bidirectional asymmetric optical flow between the two warped images, and the optical-flow-based method is used to further align the two warped images to reduce parallax. In the image blending, the softmax function and registration error are used to adjust the width of the blending area, further eliminating ghosting and reducing parallax. Finally, by comparing our method with APAP, AANAP, SPHP, SPW, TFT, and REW, it has been proven that our method can not only effectively solve perspective deformation, but also gives more natural transitions between images. At the same time, our method can robustly reduce local misalignment in various scenarios, with higher structural similarity index. A scoring method combining subjective and objective evaluations of perspective deformation, local alignment and runtime is defined and used to rate all methods, where our method ranks first.

Improved Unsupervised Stitching Algorithm for Multiple Environments SuperUDIS.

Large field-of-view images are increasingly used in various environments today, and image stitching technology can make up for the limited field of view caused by hardware design. However, previous methods are constrained in various environments. In this paper, we propose a method that combines the powerful feature extraction capabilities of the Superpoint algorithm and the exact feature matching capabilities of the Lightglue algorithm with the image fusion algorithm of Unsupervised Deep Image Stitching (UDIS). Our proposed method effectively improves the situation where the linear structure is distorted and the resolution is low in the stitching results of the UDIS algorithm. On this basis, we make up for the shortcomings of the UDIS fusion algorithm. For stitching fractures of UDIS in some complex situations, we optimize the loss function of UDIS. We use a second-order differential Laplacian operator to replace the difference in the horizontal and vertical directions to emphasize the continuity of the structural edges during training. Combined with the above improvements, the Super Unsupervised Deep Image Stitching (SuperUDIS) algorithm is finally formed. SuperUDIS has better performance in both qualitative and quantitative evaluations compared to the UDIS algorithm, with the PSNR index increasing by 0.5 on average and the SSIM index increasing by 0.02 on average. Moreover, the proposed method is more robust in complex environments with large color differences or multi-linear structures.

Advanced Image Stitching Method for Dual-Sensor Inspection.

Efficient image stitching plays a vital role in the Non-Destructive Evaluation (NDE) of infrastructures. An essential challenge in the NDE of infrastructures is precisely visualizing defects within large structures. The existing literature predominantly relies on high-resolution close-distance images to detect surface or subsurface defects. While the automatic detection of all defect types represents a significant advancement, understanding the location and continuity of defects is imperative. It is worth noting that some defects may be too small to capture from a considerable distance. Consequently, multiple image sequences are captured and processed using image stitching techniques. Additionally, visible and infrared data fusion strategies prove essential for acquiring comprehensive information to detect defects across vast structures. Hence, there is a need for an effective image stitching method appropriate for infrared and visible images of structures and industrial assets, facilitating enhanced visualization and automated inspection for structural maintenance. This paper proposes an advanced image stitching method appropriate for dual-sensor inspections. The proposed image stitching technique employs self-supervised feature detection to enhance the quality and quantity of feature detection. Subsequently, a graph neural network is employed for robust feature matching. Ultimately, the proposed method results in image stitching that effectively eliminates perspective distortion in both infrared and visible images, a prerequisite for subsequent multi-modal fusion strategies. Our results substantially enhance the visualization capabilities for infrastructure inspection. Comparative analysis with popular state-of-the-art methods confirms the effectiveness of the proposed approach.

Research on Image Stitching Algorithm Based on Point-Line Consistency and Local Edge Feature Constraints.

Image stitching aims to synthesize a wider and more informative whole image, which has been widely used in various fields. This study focuses on improving the accuracy of image mosaic and proposes an image mosaic method based on local edge contour matching constraints. Because the accuracy and quantity of feature matching have a direct influence on the stitching result, it often leads to wrong image warpage model estimation when feature points are difficult to detect and match errors are easy to occur. To address this issue, the geometric invariance is used to expand the number of feature matching points, thus enriching the matching information. Based on Canny edge detection, significant local edge contour features are constructed through operations such as structure separation and edge contour merging to improve the image registration effect. The method also introduces the spatial variation warping method to ensure the local alignment of the overlapping area, maintains the line structure in the image without bending by the constraints of short and long lines, and eliminates the distortion of the non-overlapping area by the global line-guided warping method. The method proposed in this paper is compared with other research through experimental comparisons on multiple datasets, and excellent stitching results are obtained.

Image Stitching Based on Color Difference and KAZE with a Fast Guided Filter.

Image stitching is of great importance for multiple fields such as moving object detection and tracking, ground reconnaissance and augmented reality. To ameliorate the stitching effect and alleviate the mismatch rate, an effective image stitching algorithm based on color difference and an improved KAZE with a fast guided filter is proposed. Firstly, the fast guided filter is introduced to reduce the mismatch rate before feature matching. Secondly, the KAZE algorithm based on improved random sample consensus is used for feature matching. Then, the color difference and brightness difference of the overlapping area are calculated to make an overall adjustment to the original images so as to improve the nonuniformity of the splicing result. Finally, the warped images with color difference compensation are fused to obtain the stitched image. The proposed method is evaluated by both visual effect mapping and quantitative values. In addition, the proposed algorithm is compared with other current popular stitching algorithms. The results show that the proposed algorithm is superior to other algorithms in terms of the quantity of feature point pairs, the matching accuracy, the root mean square error and the mean absolute error.

Content-Seam-Preserving Multi-Alignment Network for Visual-Sensor-Based Image Stitching.

As an important representation of scenes in virtual reality and augmented reality, image stitching aims to generate a panoramic image with a natural field-of-view by stitching multiple images together, which are captured by different visual sensors. Existing deep-learning-based methods for image stitching only conduct a single deep homography to perform image alignment, which may produce inevitable alignment distortions. To address this issue, we propose a content-seam-preserving multi-alignment network (CSPM-Net) for visual-sensor-based image stitching, which could preserve the image content consistency and avoid seam distortions simultaneously. Firstly, a content-preserving deep homography estimation was designed to pre-align the input image pairs and reduce the content inconsistency. Secondly, an edge-assisted mesh warping was conducted to further align the image pairs, where the edge information is introduced to eliminate seam artifacts. Finally, in order to predict the final stitched image accurately, a content consistency loss was designed to preserve the geometric structure of overlapping regions between image pairs, and a seam smoothness loss is proposed to eliminate the edge distortions of image boundaries. Experimental results demonstrated that the proposed image-stitching method can provide favorable stitching results for visual-sensor-based images and outperform other state-of-the-art methods.

Image Preprocessing with Enhanced Feature Matching for Map Merging in the Presence of Sensing Error.

Autonomous robots heavily rely on simultaneous localization and mapping (SLAM) techniques and sensor data to create accurate maps of their surroundings. When multiple robots are employed to expedite exploration, the resulting maps often have varying coordinates and scales. To achieve a comprehensive global view, the utilization of map merging techniques becomes necessary. Previous studies have typically depended on extracting image features from maps to establish connections. However, it is important to note that maps of the same location can exhibit inconsistencies due to sensing errors. Additionally, robot-generated maps are commonly represented in an occupancy grid format, which limits the availability of features for extraction and matching. Therefore, feature extraction and matching play crucial roles in map merging, particularly when dealing with uncertain sensing data. In this study, we introduce a novel method that addresses image noise resulting from sensing errors and applies additional corrections before performing feature extraction. This approach allows for the collection of features from corresponding locations in different maps, facilitating the establishment of connections between different coordinate systems and enabling effective map merging. Evaluation results demonstrate the significant reduction of sensing errors during the image stitching process, thanks to the proposed image pre-processing technique.

Geological Borehole Video Image Stitching Method Based on Local Homography Matrix Offset Optimization.

Due to the influence of the shooting environment and inherent image characteristics, there is a large amount of interference in the process of image stitching a geological borehole video. To accurately match the acquired image sequences in the inner part of a borehole, this paper presents a new method of stitching an unfolded borehole image, which uses the image generated from the video to construct a large-scale panorama. Firstly, the speeded-up robust feathers (SURF) algorithm is used to extract the image feature points and complete the rough matching. Then, the M-estimator sample consensus (MSAC) algorithm is introduced to remove the mismatched point pairs and obtain the homography matrix. Subsequently, we propose a local homography matrix offset optimization (LHOO) algorithm to obtain the optimal offset. Finally, the above process is cycled frame by frame, and the image sequence is continuously stitched to complete the construction of a cylindrical borehole panorama. The experimental results show that compared with those of the SIFT, Harris, ORB and SURF algorithms, the matching accuracy of our algorithm has been greatly improved. The final test is carried out on 225 consecutive video frames, and the panorama has a good visual effect, and the average time of each frame is 100 ms, which basically meets the requirements of the project.

Automatic Sequential Stitching of High-Resolution Panorama for Android Devices Using Precapture Feature Detection and the Orientation Sensor.

Image processing on smartphones, which are resource-limited devices, is challenging. Panorama generation on modern mobile phones is a requirement of most mobile phone users. This paper presents an automatic sequential image stitching algorithm with high-resolution panorama generation and addresses the issue of stitching failure on smartphone devices. A robust method is used to automatically control the events involved in panorama generation from image capture to image stitching on Android operating systems. The image frames are taken in a firm spatial interval using the orientation sensor included in smartphone devices. The features-based stitching algorithm is used for panorama generation, with a novel modification to address the issue of stitching failure (inability to find local features causes this issue) when performing sequential stitching over mobile devices. We also address the issue of distortion in sequential stitching. Ultimately, in this study, we built an Android application that can construct a high-resolution panorama sequentially with automatic frame capture based on an orientation sensor and device rotation. We present a novel research methodology (called "Sense-Panorama") for panorama construction along with a development guide for smartphone developers. Based on our experiments, performed by Samsung Galaxy SM-N960N, which carries system on chip (SoC) as Qualcomm Snapdragon 845 and a CPU of 4 × 2.8 GHz Kyro 385, our method can generate a high-resolution panorama. Compared to the existing methods, the results show improvement in visual quality for both subjective and objective evaluation.

An Inverse Perspective Mapping-Based Approach for Generating Panoramic Images of Pipe Inner Surfaces.

We propose an algorithm for generating a panoramic image of a pipe's inner surface based on inverse perspective mapping (IPM). The objective of this study is to generate a panoramic image of the entire inner surface of a pipe for efficient crack detection, without relying on high-performance capturing equipment. Frontal images taken while passing through the pipe were converted to images of the inner surface of the pipe using IPM. We derived a generalized IPM formula that considers the slope of the image plane to correct the image distortion caused by the tilt of the plane; this IPM formula was derived based on the vanishing point of the perspective image, which was detected using optical flow techniques. Finally, the multiple transformed images with overlapping areas were combined via image stitching to create a panoramic image of the inner pipe surface. To validate our proposed algorithm, we restored images of pipe inner surfaces using a 3D pipe model and used these images for crack detection. The resulting panoramic image of the internal pipe surface accurately demonstrated the positions and shapes of cracks, highlighting its potential utility for crack detection using visual inspection or image-processing techniques.

Semi-Supervised Image Stitching from Unstructured Camera Arrays.

Image stitching involves combining multiple images of the same scene captured from different viewpoints into a single image with an expanded field of view. While this technique has various applications in computer vision, traditional methods rely on the successive stitching of image pairs taken from multiple cameras. While this approach is effective for organized camera arrays, it can pose challenges for unstructured ones, especially when handling scene overlaps. This paper presents a deep learning-based approach for stitching images from large unstructured camera sets covering complex scenes. Our method processes images concurrently by using the SandFall algorithm to transform data from multiple cameras into a reduced fixed array, thereby minimizing data loss. A customized convolutional neural network then processes these data to produce the final image. By stitching images simultaneously, our method avoids the potential cascading errors seen in sequential pairwise stitching while offering improved time efficiency. In addition, we detail an unsupervised training method for the network utilizing metrics from Generative Adversarial Networks supplemented with supervised learning. Our testing revealed that the proposed approach operates in roughly ∼1/7th the time of many traditional methods on both CPU and GPU platforms, achieving results consistent with established methods.

Nighttime Image Stitching Method Based on Image Decomposition Enhancement.

Image stitching technology realizes alignment and fusion of a series of images with common pixel areas taken from different viewpoints of the same scene to produce a wide field of view panoramic image with natural structure. The night environment is one of the important scenes of human life, and the night image stitching technology has more urgent practical significance in the fields of security monitoring and intelligent driving at night. Due to the influence of artificial light sources at night, the brightness of the image is unevenly distributed and there are a large number of dark light areas, but often these dark light areas have rich structural information. The structural features hidden in the darkness are difficult to extract, resulting in ghosting and misalignment when stitching, which makes it difficult to meet the practical application requirements. Therefore, a nighttime image stitching method based on image decomposition enhancement is proposed to address the problem of insufficient line feature extraction in the stitching process of nighttime images. The proposed algorithm performs luminance enhancement on the structural layer, smoothes the nighttime image noise using a denoising algorithm on the texture layer, and finally complements the texture of the fused image by an edge enhancement algorithm. The experimental results show that the proposed algorithm improves the image quality in terms of information entropy, contrast, and noise suppression compared with other algorithms. Moreover, the proposed algorithm extracts the most line features from the processed nighttime images, which is more helpful for the stitching of nighttime images.

Feature Correspondences Increase and Hybrid Terms Optimization Warp for Image Stitching.

Feature detection and correct matching are the basis of the image stitching process. Whether the matching is correct and the number of matches directly affect the quality of the final stitching results. At present, almost all image stitching methods use SIFT+RANSAC pattern to extract and match feature points. However, it is difficult to obtain sufficient correct matching points in low-textured or repetitively-textured regions, resulting in insufficient matching points in the overlapping region, and this further leads to the warping model being estimated erroneously. In this paper, we propose a novel and flexible approach by increasing feature correspondences and optimizing hybrid terms. It can obtain sufficient correct feature correspondences in the overlapping region with low-textured or repetitively-textured areas to eliminate misalignment. When a weak texture and large parallax coexist in the overlapping region, the alignment and distortion often restrict each other and are difficult to balance. Accurate alignment is often accompanied by projection distortion and perspective distortion. Regarding this, we propose hybrid terms optimization warp, which combines global similarity transformations on the basis of initial global homography and estimates the optimal warping by adjusting various term parameters. By doing this, we can mitigate projection distortion and perspective distortion, while effectively balancing alignment and distortion. The experimental results demonstrate that the proposed method outperforms the state-of-the-art in accurate alignment on images with low-textured areas in the overlapping region, and the stitching results have less perspective and projection distortion.

A Novel Approach for UAV Image Crack Detection.

Cracks are the most significant pre-disaster of a road, and are also important indicators for evaluating the damage level of a road. At present, road crack detection mainly depends on manual detection and road detection vehicles, with which the safety of detection workers is not guaranteed and the detection efficiency is low. A road detection vehicle can speed up the efficiency to a certain extent, but the automation level is low and it is easy to block the traffic. Unmanned Aerial Vehicles (UAV) have the characteristics of low energy consumption and easy control. If UAV technology can be applied to road crack detection, it will greatly improve the detection efficiency and produce huge economic benefits. In order to find a way to apply UAV to road crack detection, we developed a new technique for road crack detection based on UAV pictures, called DenxiDeepCrack, which is a trainable deep convolutional neural network for automatic crack detection which utilises learning high-level features for crack representation. In addition, we create a new dataset based on drone images called UCrack 11 to enrich the crack database of drone images for future crack detection research.

Design of a Concentric Multi-Scale Zoom Optical System Based on Wide Object Distance and High-Precision Imaging.

To effectively balance the trade-off between a large field of view (FOV) and high resolution of an optical system, as well as to solve the problem of image stitching misalignment after focusing, firstly, this paper conducts a theoretical analysis of the design principle of the concentric multi-scale optical system and the causes of image stitching misalignment after focusing. Secondly, the design idea of using a combination structure of a two-layer front concentric imaging group and an image-space telecentric relay imaging array and then a joint full-motion zoom relay imaging system is proposed. Finally, an image-space telecentric two-step zoom concentric multi-scale optical system with a 7 × 7 relay imaging array is designed. The FOV of this optical system is 60° × 45°; the focal lengths are 50 mm and 100 mm for the center channel and 50 mm for the other channels. This concentric multi-scale zoom system has the advantages of both high-precision imaging stitching with a wide object distance and high-resolution imaging, which makes up for the defects of the conventional concentric multi-scale optical system, making it a promising application in the fields of aviation and security.

Nighttime Image Stitching Method Based on Guided Filtering Enhancement.

Image stitching refers to stitching two or more images with overlapping areas through feature points matching to generate a panoramic image, which plays an important role in geological survey, military reconnaissance, and other fields. At present, the existing image stitching technologies mostly adopt images with good lighting conditions, but the lack of feature points in scenes with weak light such as morning or night will affect the image stitching effect, making it difficult to meet the needs of practical applications. When there exist concentrated areas of brightness such as lights and large dark areas in the nighttime image, it will further cause the loss of image details making the feature point matching unavailable. The obtained perspective transformation matrix cannot reflect the mapping relationship of the entire image, resulting in poor splicing effect, and it is difficult to meet the actual application requirements. Therefore, an adaptive image enhancement algorithm is proposed based on guided filtering to preprocess the nighttime image, and use the enhanced image for feature registration. The experimental results show that the image obtained by preprocessing the nighttime image with the proposed enhancement algorithm has better detail performance and color restoration, and greatly improves the image quality. By performing feature registration on the enhanced image, the number of matching logarithms of the image increases, so as to achieve high accuracy for images stitching.

Heterogeneous Stitching of X-ray Images According to Homographic Evaluation.

The C-arm X-ray system is a common intraoperative imaging modality used to observe the state of a fractured bone in orthopedic surgery. Using C-arm, the bone fragments are aligned during surgery, and their lengths and angles with respect to the entire bone are measured to verify the fracture reduction. Since the field-of-view of the C-arm is too narrow to visualize the entire bone, a panoramic X-ray image is utilized to enlarge it by stitching multiple images. To achieve X-ray image stitching with feature detection, the extraction of accurate and densely matched features within the overlap region between images is imperative. However, since the features are highly affected by the properties and sizes of the overlap regions in consecutive X-ray images, the accuracy and density of matched features cannot be guaranteed. To solve this problem, a heterogeneous stitching of X-ray images was proposed. This heterogeneous stitching was completed according to the overlap region based on homographic evaluation. To acquire sufficiently matched features within the limited overlap region, integrated feature detection was used to estimate a homography. The homography was then evaluated to confirm its accuracy. When the estimated homography was incorrect, local regions around the matched feature were derived from integrated feature detection and substituted to re-estimate the homography. Successful X-ray image stitching of the C-arm was achieved by estimating the optimal homography for each image. Based on phantom and ex-vivo experiments using the proposed method, we confirmed a panoramic X-ray image construction that was robust compared to the conventional methods.