Image Strips Research Articles

Nanograting-Based Dynamic Structural Colors Using Heterogeneous Materials.

Dynamic structural colors can change in response to different environmental stimuli. This ability remains effective even when the size of the species responsible for the structural color is reduced to a few micrometers, providing a promising sensing mechanism for solving microenvironmental sensing problems in micro-robotics and microfluidics. However, the lack of dynamic structural colors that can encode rapidly, easily integrate, and accurately reflect changes in physical quantities hinders their use in microscale sensing applications. Herein, we present a 2.5-dimensional dynamic structural color based on nanogratings of heterogeneous materials, which were obtained by interweaving a pH-responsive hydrogel with an IP-L photoresist. Transverse gratings printed with pH-responsive hydrogels elongated the period of longitudinal grating in the swollen state, resulting in pH-tuned structural colors at a 45° incidence. Moreover, the patterned encoding and array printing of dynamic structural colors were achieved using grayscale stripe images to accurately encode the periods and heights of the nanogrid structures. Overall, dynamic structural color networks exhibit promising potential for applications in information encryption and in situ sensing for microfluidic chips.

On-orbit Moving Target Detection Method Based on Dual Images from Taijing-IV 01 Satellite

Abstract. With the evolution of satellite video technology, the domain has garnered increasing attention. Concurrently, advancements in deep learning have yielded numerous outcomes in target detection. This paper introduces a novel method for detecting moving targets, offering a broader detection range compared to traditional satellite video techniques, facilitating orbital target recognition from dual panchromatic image strips. Our experimental setup on the Taijing-IV 01 satellite, launched on February 27, 2022, successfully acquired two image strips separated by one second. These strips contain speed and directional information of moving objects, extractable through the frame differencing technique. We propose combining frame differencing with lightweight deep learning for target detection, extracting regions of interest (ROIs) to focus on areas with potential moving targets. This approach reduces the workload of wholeimage target detection, decreasing data processing volume by 89%. By optimizing the YOLOv8 network and using techniques like feature map fusion of low-level and high-resolution features, we enhance sensitivity to small targets. Consequently, the model size is reduced by 79%, the mean Average Precision (mAP) increases by approximately 1.8% and 4.5%, and detection speed rises by 26%. This method introduces a new paradigm in remote sensing data services, facilitating rapid acquisition and real-time transmission of positions and image information of moving targets to the ground. This significantly reduces bandwidth requirements for transmitting remote sensing information, presenting a novel strategy for data acquisition and processing in large-scale Earth observation systems and geoscientific applications.

Research on a Feature Point Detection Algorithm for Weld Images Based on Deep Learning

Laser vision seam tracking enhances robotic welding by enabling external information acquisition, thus improving the overall intelligence of the welding process. However, camera images captured during welding often suffer from distortion due to strong noises, including arcs, splashes, and smoke, which adversely affect the accuracy and robustness of feature point detection. To mitigate these issues, we propose a feature point extraction algorithm tailored for weld images, utilizing an improved Deeplabv3+ semantic segmentation network combined with EfficientDet. By replacing Deeplabv3+’s backbone with MobileNetV2, we enhance prediction efficiency. The DenseASPP structure and attention mechanism are implemented to focus on laser stripe edge extraction, resulting in cleaner laser stripe images and minimizing noise interference. Subsequently, EfficientDet extracts feature point positions from these cleaned images. Experimental results demonstrate that, across four typical weld types, the average feature point extraction error is maintained below 1 pixel, with over 99% of errors falling below 3 pixels, indicating both high detection accuracy and reliability.

Aerial Hybrid Adjustment of LiDAR Point Clouds, Frame Images, and Linear Pushbroom Images

In airborne surveying, light detection and ranging (LiDAR) strip adjustment and image bundle adjustment are customarily performed as separate processes. The bundle adjustment is usually conducted from frame images, while using linear pushbroom (LP) images in the bundle adjustment has been historically challenging due to the limited number of observations available to estimate the exterior image orientations. However, data from these three sensors conceptually provide information to estimate the same trajectory corrections, which is favorable for solving the problems of image depth estimation or the planimetric correction of LiDAR point clouds. Thus, our purpose with the presented study is to jointly estimate corrections to the trajectory and interior sensor states in a scalable hybrid adjustment between 3D LiDAR point clouds, 2D frame images, and 1D LP images. Trajectory preprocessing is performed before the low-frequency corrections are estimated for certain time steps in the following adjustment using cubic spline interpolation. Furthermore, the voxelization of the LiDAR data is used to robustly and efficiently form LiDAR observations and hybrid observations between the image tie-points and the LiDAR point cloud to be used in the adjustment. The method is successfully demonstrated with an experiment, showing the joint adjustment of data from the three different sensors using the same trajectory correction model with spline interpolation of the trajectory corrections. The results show that the choice of the trajectory segmentation time step is not critical. Furthermore, photogrammetric sub-pixel planimetric accuracy is achieved, and height accuracy on the order of mm is achieved for the LiDAR point cloud. This is the first time these three types of sensors with fundamentally different acquisition techniques have been integrated. The suggested methodology presents a joint adjustment of all sensor observations and lays the foundation for including additional sensors for kinematic mapping in the future.

Destriping model for adaptive removal of arbitrary oriented stripes in remote sensing images

Abstract Destriping in remote sensing image processing remains a challenging problem, particularly when dealing with stripe noise of arbitrary orientations. Conventional methods struggle to eliminate oblique stripes, leaving a crucial gap in the production of higher-level remote sensing products. In response, we propose a novel destriping model, the Adaptive Stripe Noise Removal (ASNR) Method, designed to adapt to different orientations of stripe noise, aiming for accuracy, robustness, speed, and simplicity. The paper first addresses the conventional challenges in stripe removal, emphasizing the unintended loss of information during the process. To overcome this, we conduct a detailed study of stripe noise characteristics, employing traditional Fast Fourier Transform (FFT) for stripe orientation approximation. However, conventional techniques using spatial representations risk damaging detailed structures. To go beyond these limitations, the proposed method combines spectral processing technology with an image guidance mechanism. This approach aims to generate a guided image that retains both denoised features and important details. In the frequency domain, the method corrects the stripe image by estimating a guidance image. Experimental results, both qualitative and quantitative, demonstrate the superiority and stability of the proposed method in removing stripe noise and preserving image details without introducing artifacts. The novel approach fills a critical gap in destriping methods, offering a fast, accurate, and adaptable solution for arbitrary orientations of stripe noise in remote sensing images.

Structural-information-awareness-based regularization model for infrared image stripe noise removal

Structural-information-awareness-based regularization model for infrared image stripe noise removal

Research and Application of Destriping Algorithm based on Weighted Block Sparse Representation to FengYun Satellite Imager Data

Abstract In remote sensing imaging systems, stripe noise is a pervasive issue primarily caused by the inconsistent response of multiple detectors. Stripe noise not only affects image quality but also severely hinders subsequent quantitative derived products and applications. Therefore, it is crucial to eliminate stripe noise while preserving detailed structure information in order to enhance image quality. Although existing destriping methods have achieved certain effects to some extent, they still face problems such as loss of image details, image blur, and ringing artifacts. To address these issues, this study proposes an image stripe correction algorithm based on weighted block sparse representation. This research applies techniques such as differential low-rank constraint and edge weight factor to remove stripe noise while retaining image detail information. The algorithm also uses the alternating direction method of multipliers (ADMM) to solve the MCP regularized least squares optimization problem model, improving the processing efficiency of the model. The results of this study have been applied and validated in imager data from the Medium Resolution Spectral Imager (MERSI-II) onboard FengYun-3D satellite, the Multi-channel Scanning Radiometer (AGRI) onboard FengYun-4A satellite and Precipitation Microwave Radiometer (MWRI–RM) onboard FengYun-3G. Compared to typical stripe correction methods, the proposed method achieves better stripe removal while preserving image detail information. The destriped image data can be used to generate high-quality quantitative products for various applications. Overall, by combining insights from prior research and innovative techniques, this study provides a more effective and robust solution to the stripe noise problem in remote sensing and weather forecast.

3D measurement of precise part in complex circumstance using line structured light and improved U-Net

Abstract Line structured light scanning is extensively utilized for the 3D measurement of precise metal parts, but the curvature surfaces and specific materials of these parts generate specular reflection, making it challenging to accurately extract the center of the laser stripe in complex circumstances. Therefore, the primary challenges are the noise separation in the laser stripe image and the extraction of the laser stripe center under complex circumstances. To solve the above problems, an improved U-Net semantic segmentation algorithm is proposed by adding an attention mechanism and modifying skip connections to the classical U-Net network structure for accurate laser stripe segmentation. Secondly, the dual smoothing method of mean smoothing and Savitzky–Golay smoothing is combined with the Hessian matrix algorithm to complete the subpixel extraction of the center point of the laser stripe. Finally, taking the blade and shaft part as the measurement objects, the experimental results demonstrate that the method can obtain more complete, smoother, and denser results than the traditional method under highly reflective surfaces, vital interference spots, and strong ambient light. The proposed method is used for the 3D measurement of the shaft parts, and the diameter measurement maximum error is 0.029 mm, verifying the feasibility of the proposed method.

Efficient and Accurate pH Determination with pH Test Strips Based on Machine Learning.

The determination of pH values is crucial in various fields, such as analytical chemistry, medical diagnostics, and biochemical research. pH test strips, renowned for their convenience and cost-effectiveness, are commonly utilized for pH qualitative estimation. Recently, quantitative methods for determining pH values using pH test strips have been developed. However, these methods can be prone to errors due to environmental factors, such as lighting conditions, which affect the imaging quality of the pH test strips. To address these challenges, we developed an innovative approach that combines machine learning techniques with pH test strips for the quantitative determination of pH values. Our method involves extracting artificial features from the pH test strip images and combining them across multiple dimensions for comprehensive analysis. To ensure optimal feature selection, we developed a feature selection strategy based on SHAP importance. This strategy helps in identifying the most relevant features that contribute to accurate pH prediction. Furthermore, we integrated multiple machine learning algorithms, employing a robust stacking fusion strategy to establish a highly reliable pH value prediction model. Our proposed method automates the determination of pH values through pH test strips, effectively overcoming the limitations associated with environmental lighting interference. Experimental results demonstrate that this method is convenient, effective, and highly reliable for the determination of pH values.

A comprehensive laser image dataset for real-time measurement of wheelset geometric parameters

Railway transportation has experienced significant growth worldwide, offering numerous benefits to society. Most railway accidents are caused by wheelset faults so it’s significant to monitor wheelset conditions. Therefore, we need to collect wheelset images, repaint them, extract laser stripe centerlines, construct 3D contour, and measure their geometric parameters to judge the wheelset’s conditions. Deep learning can fulfill the tasks satisfyingly because it’s adaptable, robust, and generalize compared with traditional methods. To train the deep learning models effectively, we need rich and high-quality wheelset datasets. So far, there are no applicable public train wheelset datasets available, which greatly hinders the research on train wheelsets. Thus we construct a publicly available Wheelset Laser Image Dataset (WLI-Set). WLI-Set consists of four sub-datasets, Original, Inpainting, Segmentation, and Centerline. The dataset contains abundant annotated multiline laser stripe images that can facilitate the research on train wheelsets effectively.

Quantitative detection of L-cysteine with a ratiometric probe combined with smartphone imaging and fluorescent test strips

Cysteine is an important thiol involved in the vital activities of the human body, and many physiological processes in the body are associated with it. Accurate detection of cysteine is of great importance in monitoring the development of diseases. In this paper, we prepared a ratiometric flavonoid fluorescent probe, HDFA, that exhibits specific selectivity for L-cysteine. HDFA was the first fluorescent probe that can distinguish between L-cysteine and D-cysteine. When detecting L-cysteine, the fluorescence color changed from blue to yellow, and the fluorescence quantum yield increased from 16.96 % to 21.30 %. The probe has a short response time (90 s) to L-cysteine, a large Stokes shift (186 nm), good selectivity, and anti-interference, and the detection limit was 0.32 μM. The detection mechanism was confirmed by mass spectrometry. In addition, HDFA has good applications in cell imaging, smartphone fluorescence imaging and fluorescence test strip detection.

АРХИТЕКТУРНО-ГРАДОСТРОИТЕЛЬНОЕ РАЗВИТИЕ УЛИЦЫ СИМФЕРОПОЛЬСКОЙ В ГОРОДЕ ЕВПАТОРИЯ

Abstract. The relevance of the development of Simferopolskaya Street in the city of Yevpatoria, Republic of Crimea, is due to the increased need to improve the quality of the coastal urban environment. Recently, the western part of the Crimean coast has been gaining increasing interest among tourists. One of the most popular tourist cities is Evpatoria. The main architectural and urban planning problems of the coastal zone in the area of the street have been identified. Simferopolskaya: obsolescence of low-rise residential buildings, unattractive aesthetic appearance of the facades of buildings and structures, lack of an embankment and comfortable landscaping of the territory. Currently, in the improvement of the territory under consideration, there is no single architectural style - a harmonious appearance of the street. The work analyzes theoretical and practical materials related to the object of research, and analyzes world analogues. Scientific principles and practical recommendations for the reconstruction of Simferopolskaya Street with the adjacent seaside area - the embankment and the beach - are proposed. To test the research results, an experimental design model was developed.
 Subject: Reconstruction of the territory within the boundaries of Simferopolskaya Street in the city of Evpatoria, landscaping.
 Materials and methods: During the study, an analysis of projects of world analogues was used. To identify the historical pattern of development of the territory, a comparative analysis of coastal territories of the Soviet period and at present is used. In the process of selecting materials on the topic of publication and analysis of the urban planning situation, the following methods were used: deduction, theoretical analysis and synthesis, the method of analogy, field surveys, photographic recording, critical and urban planning analysis, experimental design.
 Results: To increase the duration of the holiday season and increase the attractiveness of the study area, the main indicators have been identified: the formation of an identifying architectural image of the coastal strip of Simferopolskaya Street in the city of Evpatoria, attracting investors to implement projects, expanding the scope of services provided to tourists.
 It is proposed to apply the developed scientific principles for the development of the street and adjacent coastal areas:
 - the principle of integrity of the structure of space;
 - ecological principle;
 - principle of investment attractiveness;
 - the principle of increasing positive factors in the use and development of existing building functions and territory;
 - the principle of preservation and renewal;
 - the principle of continuity;
 - the principle of architectural and artistic expressiveness and functional diversity.
 The formation and transformation of urban public recreational zones of coastal areas must have a well-thought-out strategy for the systematic, phased implementation of work on the construction of facilities and improvement of coastal areas. To comply with the above principles, it is necessary to develop a strategy or concept for the development of the territory and adhere to it when reconstructing the territory.
 Conclusions: Based on the results of the analysis of global and domestic design and reconstruction of embankments, current trends in the formation of urban recreational zones of coastal territories were identified, which should also be applied to the city of Evpatoria:
 1) development of pedestrian traffic infrastructure through the development of the coastal strip as a system of pedestrian spaces in contact with the shoreline of water bodies;
 2) the maximum possible distance of water bodies from intense traffic flows, giving preference to pedestrian spaces;
 3) improvement of the green zone as an infrastructure of coastal areas, by forming an integral, continuous strip or islands of greenery;
 4) the formation within the boundaries of coastal territories of a multifunctional system that unites recreational areas and public spaces, the interaction of pedestrian, green, and water infrastructures as a single whole;
 5) development of coastal areas as complex objects of urban culture.
 The practical application of the developed principles and recommendations embedded in the experimental design model - the concept of reconstruction of the seaside street of Simferopol in the city of Yevpatoria will contribute to the qualitative development of the urban environment.

Innovative Stripe Noise Image Correction Method for Remote Sensing

Due to their propensity for stripe noise distortions, infrared remote sensing images present substantial difficulty for interpretation. Our ability to address this issue by offering an easy, efficient, and fast technique for image stripe noise correction is what makes our work unique. Our proposed solution tackles stripe noise by subtracting the mean value along the stripes from the noisy image. Additionally, we leverage the wavelet transform on the average signal to exploit the inherent sparsity of noise in the wavelet domain. This approach not only enhances denoising performance without introducing blurring effects but also enables us to recover image details with remarkable precision, all without the need for intricate algorithms, iterative processes, or training models. To validate the effectiveness of our approach, we conducted evaluations using a dataset of real-world infrared remote sensing images. This dataset encompasses a wide range of examples, featuring both real and artificially induced noise scenarios.

Steel strip surface defect detection based on multiscale feature sensing and adaptive feature fusion

A surface defect detection method for hot-rolled steel strips was proposed to address the challenges of detecting small target defects, significant differences in morphology, and unclear defect characteristics. This method is based on multiscale feature perception and adaptive feature fusion. First, based on the spatial distribution characteristics of the steel strip image, redundant background interference is removed using automatic gamma correction and Otsu thresholding. Second, based on the characteristics of surface defects in steel strips, this paper proposes TDB-YOLO (YOLO with a small target detection layer), a Bidirectional Feature Pyramid Network (BiFPN), and Double Cross Stage Partial (CSP) Bottleneck with three convolutions (DC3). To detect small object defects, a small target detection layer with a smaller receptive field focuses on fine-grained features, reducing the model’s probability of missed detection. In terms of feature extraction, DC3 enhances the interaction of feature information from different spatial scales, enabling the model to effectively handle features of varying scales. In terms of feature fusion, the BiFPN is used to adaptively fuse deep-level and shallow-level feature information, enhancing the semantic richness of the feature information. Ultimately, the proposed model in this paper achieved an accuracy of 90.3% and a recall rate of 88.0% for surface defects in steel strips. The mean average precision was 90.4%, and the frames per second was 33. The detection performance of this model outperformed those of other detection models, demonstrating its ability to effectively meet the real-time detection requirements of surface defects in industrial scenarios on steel strips.

A Detecting Method of Users’ Reading State in Library via Modulated Light Illumination

ABSTRACT In order to overcome the shortcomings of the traditional methods for detecting the users’ reading state in a smart library building, a more effective detecting method of library users’ reading state is proposed. In this method, some posture characteristic parameters are put forward in order to represent the users’ reading state after analyzing the relationship between the users’ posture and their reading state. And then, one stripe light illuminating system of 3-D posture is designed by developing of a stripe projector and a video monitoring system, in which, the image of users’ posture with periodic stripe modulation is received on the video monitoring system. At last, two users’ posture stripe images are extracted by time sequence, one as a reference image and the other as a deformed image, and these images are superimposed by their pixel subtracting after extracting the stripes, so as to obtain a superimposed image that reflects the users’ posture variation characteristic. The users’ posture characteristic parameters can be gotten by calculating the area pixels and number of deformation regions of continuous pixels, and the changed pixels per unit time and per user based on the superimposed image. Therefore, the users’ reading state can be detected based on the relationship between the characteristic parameters and users’ reading state. This method can realize the accurate detecting of the reading users’ status and provide the basis to build the smart library.

Automatic optimization of projection intensity for high dynamic range 3D surface measurement

Stripe projection profilers are widely used in many fields because of their high speed, high accuracy, and clear surface textures. In the structured light projection method, the complex reflectance distribution on the surface of the high-dynamic-range objective is an important factor that affects the measurement accuracy, as it can result in image over- and under-exposure. In this study, a method is proposed to adaptively determine the number of stripe patterns and the corresponding number of light intensities of the stripe patterns based on the complex reflectance of the surface of the object being measured. A multi threshold Otsu algorithm is used to classify the histogram of the surface reflectance distribution of the measured object, and several sets of stripe patterns with optimal light intensity are generated according to the light intensity response function. The original stripe patterns acquired at different light intensities are synthesized pixel by pixel into a high dynamic range (HDR) stripe image, and a four-step phase-shifting algorithm is used to obtain the unwrapped phase from the synthesized image. Experimental results show that this method can accurately measure the target of a surface-reflectance-transformed HDR.

Center extraction method for reflected metallic surface fringes based on line structured light.

Using line structured light to measure metal surface topography, the extraction error of the stripe center is significant due to the influence of the optical characteristics of the metal surface and the scattering noise. This paper proposes a sub-pixel stripe center extraction method based on adaptive threshold segmentation and a gradient weighting strategy to address this issue. First, we analyze the characteristics of the stripe image of the measured metal's surface morphology. Relying on the morphological features of the image, the image is segmented to remove the effect of background noise and to obtain the region of interest in the image. Then, we use the gray-gravity method to get the rough center coordinates of the stripes. We extend the stripes in the width direction using the rough center coordinates as a reference to determine the center of the stripes for extraction after segmentation. Next, we adaptively determine the boundary threshold utilizing the region's grayscale. Finally, we use the gradient weighting strategy to extract the sub-pixel stripe center. The experimental results show that the proposed method effectively eliminates the interference of metal surface scattering on 3D reconstruction. The average height error of the measured standard block is 0.025mm, and the repeatability of the measurement accuracy is 0.026mm.

Low-power laser image enhancement via deep feature recovery for HDR 3D measurement.

Laser 3D measurement has gained widespread applications in industrial metrology . Still, it is usually limited by surfaces with high dynamic range (HDR) or the colorful surface texture of measured surfaces, such as metal and black industrial parts. Currently, conventional methods generally work with relatively strong-power laser intensities, which could potentially damage the sample or induce eye-safety concerns. For deep-learning-based methods, due to the different reflectivity of the measured surfaces, the HDR problem may require cumbersome adjustment of laser intensity in order to acquire enough training data. Even so, the problem of inaccurate ground truth may occur. To address these issues, this paper proposes the deep feature recovery (DFR) strategy to enhance low-light laser stripe images for achieving HDR 3D reconstruction with low cost, high robustness, and eye safety. To the best of our knowledge, this is the first attempt to tackle the challenge of high measurement costs associated with measuring HDR surfaces in laser 3D measurement. In learning the features of low-power laser images, the proposed strategy has a superior generalization ability and is insensitive to different low laser powers and variant surface reflectivity. To verify this point, we specially design the experiments by training the network merely using the diffusely reflective masks (DRM951) and testing the performance using diffusely reflective masks, metal surfaces, black industrial parts (contained in the constructed datasets DRO690, MO191, and BO107) and their hybrid scenes. Experimental results demonstrate that the proposed DFR strategy has good performances on robustness by testing different measurement scenes. For variously reflective surfaces, such as diffusely reflective surfaces, metal surfaces, and black parts surfaces, the reconstructed 3D shapes all have a similar quality to the reference method.

An inexact proximal majorization-minimization algorithm for remote sensing image stripe noise removal

An inexact proximal majorization-minimization algorithm for remote sensing image stripe noise removal

Multi-object distance determination by analysis of CoC variation for dynamic structured light.

A multi-object distance determination method can be achieved by 932 nm structured light with one camera as the data receiver. The structured light generated by a liquid crystal on silicon spatial light modulator (LCoS-SLM) facilitates dynamic image projection on targets. A series of moving light strip images were captured and collected for data analysis. This method lifted the limitation of single-object distance determination and the limitation of the angle requirement between the camera and the light source in the triangulation method. The average error of this method was approximately 3% in the range of 700 mm to 1900 mm away from LCoS-SLM without further optimization. It provides a potential compact design for indoor multi-object distance determination in the future.