Image Defects Research Articles

Dysmorphic and illness anxiety-related unwanted intrusive thoughts in individuals with obsessive-compulsive disorder.

Unwanted intrusive thoughts (UITs) are considered normal variants of the obsessions found in obsessive-compulsive disorder (OCD). Similarly, intrusive and persistent preoccupations about appearance defects in body dysmorphic disorder (BDD) and images and thoughts about illness in illness anxiety disorder (IAD) are abnormal variants of the thoughts and concerns about appearance and health found in non-clinical individuals. This study examines whether patients with OCD have frequent and distressing UITs with contents related to BDD and IAD, in addition to OCD-related UITs. Thirty-nine participants with OCD (Mage = 32.45, standard deviation [SD] = 11.57; 63% men) completed the Questionnaire of Unpleasant Intrusive Thoughts (QUIT), designed to assess obsessional, dysmorphic and illness anxiety UITs and their associated functional consequences (emotions, appraisals, and neutralizing or control strategies). Up to 71% of the individuals with OCD experienced the three UIT modalities. As expected, OCD-related UITs were the most frequent, although were no differences among the three UIT contents in terms of the disturbance they caused. The OCD intrusions were the most interfering, egodystonic and dysfunctionally appraised, and they instigated more neutralizing behaviours. Nonetheless, all UITs instigate the need to do something to alleviate the discomfort caused by their intrusion and attempts to suppress them. UITs with different contents not always related to typical obsessive themes are a common experience in patients with OCD. These UITs could have detrimental consequences for the course of the disorder itself, and they should be adequately addressed in both the assessment and treatment of these patients.

Using Machine Learning Techniques to Detect Defects in Images of Metal Structures

Using Machine Learning Techniques to Detect Defects in Images of Metal Structures

Restoration and Enhancement of Historical Stereo Photos.

Restoration of digital visual media acquired from repositories of historical photographic and cinematographic material is of key importance for the preservation, study and transmission of the legacy of past cultures to the coming generations. In this paper, a fully automatic approach to the digital restoration of historical stereo photographs is proposed, referred to as Stacked Median Restoration plus (SMR+). The approach exploits the content redundancy in stereo pairs for detecting and fixing scratches, dust, dirt spots and many other defects in the original images, as well as improving contrast and illumination. This is done by estimating the optical flow between the images, and using it to register one view onto the other both geometrically and photometrically. Restoration is then accomplished in three steps: (1) image fusion according to the stacked median operator, (2) low-resolution detail enhancement by guided supersampling, and (3) iterative visual consistency checking and refinement. Each step implements an original algorithm specifically designed for this work. The restored image is fully consistent with the original content, thus improving over the methods based on image hallucination. Comparative results on three different datasets of historical stereograms show the effectiveness of the proposed approach, and its superiority over single-image denoising and super-resolution methods. Results also show that the performance of the state-of-the-art single-image deep restoration network Bringing Old Photo Back to Life (BOPBtL) can be strongly improved when the input image is pre-processed by SMR+.

Selection and evaluation of spherical acquisition trajectories for industrial computed tomography

In conventional industrial computed tomography, the source–detector system rotates in equiangular steps in-plane relative to the part of investigation. While being by far the most frequently used acquisition trajectory today, this method has several drawbacks like the formation of cone beam artefacts or limited usability in case of geometrical restrictions. In such cases, the usage of alternative spherical trajectories can be beneficial to improve image quality and defect visibility. While investigations have been performed to relate the influence of the trajectory choice in the typical metrological case of a high number of available projections, so far barely any work has been done for the case of few source–detector poses, which is more relevant in the field of non-destructive testing. In this work, we provide an overview of quantitative metrics that can be used to assess the image quality of reconstructed computed tomography volumes, discuss their advantages and drawbacks and propose a framework to investigate the performance of several non-standard trajectories with respect to previously defined regions of interest. Inspired by pseudorandom sampling methods for Monte–Carlo-algorithms, we also suggest an entirely new trajectory design, the low-discrepancy spherical trajectory, which extends the concept of equiangular planar trajectories into three dimensions and can be used for benchmarking and comparison with other spherical trajectories. Last, we use an optimization method to calculate task-specific acquisition trajectories and relate their performance to other spherical designs.

Research on Defect Classification and Detection Technology of Image Processing Collected by Computer

The paper uses median filter computer algorithms to detect and classify machine component image defects using image processing, pattern recognition and machine vision theories. In the experiment, automatic extraction of defect images and minimization of defect images are completed to reduce processing volume and storage space requirements, and automatically determine defect categories. The article processes the collected defect images, and the experimental results prove that the method can correctly realize the detection of track surface defects and has certain applicability.

Defect Detection in Atomic Resolution Transmission Electron Microscopy Images Using Machine Learning

Point defects play a fundamental role in the discovery of new materials due to their strong influence on material properties and behavior. At present, imaging techniques based on transmission electron microscopy (TEM) are widely employed for characterizing point defects in materials. However, current methods for defect detection predominantly involve visual inspection of TEM images, which is laborious and poses difficulties in materials where defect related contrast is weak or ambiguous. Recent efforts to develop machine learning methods for the detection of point defects in TEM images have focused on supervised methods that require labeled training data that is generated via simulation. Motivated by a desire for machine learning methods that can be trained on experimental data, we propose two self-supervised machine learning algorithms that are trained solely on images that are defect-free. Our proposed methods use principal components analysis (PCA) and convolutional neural networks (CNN) to analyze a TEM image and predict the location of a defect. Using simulated TEM images, we show that PCA can be used to accurately locate point defects in the case where there is no imaging noise. In the case where there is imaging noise, we show that incorporating a CNN dramatically improves model performance. Our models rely on a novel approach that uses the residual between a TEM image and its PCA reconstruction.

A Simple Subjective Evaluation of Enface OCT Reflectance Images Distinguishes Glaucoma From Healthy Eyes.

PurposeWe present a subjective approach to detecting glaucomatous defects in enface images and assess its diagnostic performance. We also test the hypothesis that if reflectivity changes precede thickness changes in glaucoma there should be reduced correlation between the modalities in glaucoma compared to controls.MethodsTwenty glaucoma participants and 20 age-matched controls underwent high-resolution OCT scans of one eye. 4 µm-thick enface slabs were constructed through the retina. Enface indices were depths of first gap in visible retinal nerve fiber bundles (RNFBs) and last visible bundle, subjectively evaluated in six sectors of a 3.5 mm circle around the optic disc. Retinal nerve fiber layer thickness (RNFLT) along the same circle was extracted at angles corresponding to enface indices. Between-group differences were tested by linear mixed models. Diagnostic performance was measured by partial receiver operating characteristic area (pAUC).Results First gap and last visible bundle were closer to the inner limiting membrane in glaucoma eyes (both P < 0.0001). Enface indices showed excellent diagnostic performance (pAUCs 0.63–1.00), similar to RNFLT (pAUCs 0.63–0.95). Correlation between enface and RNFLT parameters was strong in healthy (r = 0.81–0.92) and glaucoma eyes (r = 0.73–0.80).ConclusionsThis simple subjective method reliably identifies glaucomatous defects in enface images with diagnostic performance at least as good as existing thickness indices. Thickness and reflectivity were similarly related in healthy and glaucoma eyes, providing no strong evidence of reflectivity loss preceding thinning. Objective analyses may realize further potential of enface OCT images in glaucoma.Translational RelevanceNovel enface OCT indices may aid glaucoma diagnosis.

Attention U-Net with Feature Fusion Module for Robust Defect Detection

U-Net shows a remarkable performance and makes significant progress for segmentation task in medical images. Despite the outstanding achievements, the common case of defect detection in industrial scenes is still a challenging task, due to the noisy background, unpredictable environment, varying shapes and sizes of the defects. Traditional U-Net may not be suitable for low-quality images with low illumination and corruption, which are often presented in the practical collections in real-world scenes. In this paper, we propose an attention U-Net with feature fusion module for combining multi-scale features to detect the defects in noisy images automatically. Feature fusion module contains convolution kernels of different scales to capture shallow layer features and combine them with the high-dimensional features. Meanwhile, attention gates are used to enhance the robustness of skip connection between the feature maps. The proposed method is evaluated on two datasets. The best precision rate and MIoU of defect detection are 95.6% and 92.5%. The best F-score of concrete crack detection is 95.0%. Experimental results show that the proposed approach achieves promising results in both datasets. It demonstrates that our approach consistently outperforms other U-Net-based approaches for defect detection in low-quality images. Experimental results have shown the possibility of developing a mixture system that can be deployed in many applications, such as remote sensing image analysis, earthquake disaster situation assessment, and so on.

Floquet wave theory-based time-corrected ultrasonic total focusing method for fiber-reinforced composite laminate

Fiber reinforced polymers (FRPs) are increasingly used in thick primary load-bearing structures. Nevertheless, manufacturing and in-service defects occur with a higher chance as the FRP thickness increases and thus the potential structure defects should be detected and evaluated. To image defects in thick FRP over 10 mm thick, this study proposes a frequency-and-structure dependent time-corrected ultrasonic total focusing method (FS-TFM) based on Floquet wave theory, which differs from conventional TFM that the wave velocity correction along different propagation direction bases on not only the structural anisotropy and inhomogeneity of FRP, but also on the probing frequency. First an analytical Floquet-wave-based dynamic homogenization for a crossply FRP laminate is performed, to obtain the homogenization region and then the wave anisotropy and dispersion therein. Then numerical finite element analysis follows, to further interrogate the time domain feature of wave signals. With the understanding of wave propagation, by accurately correcting the wave velocity anisotropy related to both the inspected crossply FPR and wave frequency, the FS-TFM imaging technique is proposed to focus the defect-scattered wave energy on the defect location. In addition, the signal-to-noise ratio (SNR) analysis of side-drilled-hole (Ø2 mm) imaging via different imaging algorithms shows that the proposed FS-TFM achieves the highest SNR at the frequency approaching the upper limit of the homogenization region. Finally, the experimental validation further indicates the potential of the proposed FS-TFM for accurate defect imaging in thick FRP.

Comparing optimization methods for deep learning in image processing applications

Abstract Training a deep learning network requires choosing its weights such that the output minimizes a given loss function. In practice, stochastic gradient descent is frequently used for solving the optimization problem. Several variants of this approach have been suggested in the literature. We study the impact of the choice of the optimization method on the outcome of the learning process at the example of two image processing applications from quite different fields. The first one is artistic style transfer, where the content of one image is combined with the style of another one. The second application is a real world classification task from industry, namely detecting defects in images of air filters. In both cases, clear differences between the results of the individual optimization methods are observed.

An ameliorated deep dense convolutional neural network for accurate recognition of casting defects in X-ray images

Recognizing defects in X-ray images plays an important role in the detection of internal defects in titanium alloy castings. However, the existing manual defects recognition methods have common drawbacks such as unstable artificial recognition, misrecognition, huge workload, and low efficiency of recognition. To make up for the shortcomings, an ameliorated deep dense convolutional neural network (BX-Net) was presented to accurately recognize casting defects in X-ray images and effectively extract highly discriminative features of different categories. DenseNet121 was used as the backbone of BX-Net and the feature extracted by DenseNet121 was fully shared by the two inputs of a bilinear pooling layer. Transfer learning was applied to reduce the demand for data and hyperparameters’ tuning. The backbone of BX-Net was firstly trained on the ImageNet and then all layers of BX-Net was fine-tuned on nine-hundred X-ray images of TiAl aero casting components. Other six deep convolutional neural networks (DenseNet121, EfficientNetB4, EfficientNetB7, ResNet50, VGG16 and Xception) were also trained to be compared with the presented BX-Net. Two Support Vector Machines were trained on the LBP features data set and HOG features data set of nine-hundred X-ray images of TiAl aero casting components respectively Experiments comparing BX-Net with other six deep learning models and two machine learning models on one hundred X-ray images (test set) of TiAl aero casting components were carried out. The comparison results show that BX-Net has the least parameters except for the Densenet121. The recall and accuracy of BX-Net were 99% and 99% respectively. In addition, the comparison results also show that BX-Net was the only model that learned discriminative feature representation of the casting X-ray image data set. The BX-Net proposed in this paper is expected to overcome the shortcomings of manual defects recognition.

Novel Feature Fusion Module-Based Detector for Small Insulator Defect Detection

The failure of an insulator may compromise the safety of the entire power transmission system. Therefore, insulator defect detection is vital for the safe operation of power systems. However, insulator defects in an insulator image may have varying sizes, and several currently available methods do not have satisfactory detection accuracy for small defects. To address this issue, we propose an improved detection network for small insulator defects with a batch normalization convolutional block attention module (BN-CBAM) and a feature fusion module. The BN-CBAM is designed to better exploit channel information and enhance the effect of different channels on the feature map. In addition, we propose a feature fusion module that fuses multi-scale features from different layers to improve small object detection performance. Moreover, to address the scarcity of aerial images, a data augmentation method based on the fusion of the target segment and background is introduced. Experiments demonstrate that the proposed method achieves better small insulator defect detection performance than other state-of-the-art approaches. In addition, data augmentation methods enrich sample diversity and enhance the generalizability of the network.

An effective defect detection method based on improved Generative Adversarial Networks (iGAN) for machined surfaces

Abstract In actual industrial applications, the predictive performance of the deep learning model mainly depends on the size and quality of the training samples, while the collection period of defective samples is long or even difficult to obtain. In this article, an effective method based on improved Generative Adversarial Networks (iGAN) is proposed to detect defects of machined surfaces on the basis of positive sample training and image restoration. This model could help to detect surface defects through positive sample learning without the training for defect samples and traditional artificial labels. In this method, an improved reconstructed image network model is constructed based on the Generative Adversarial Networks. Through this method, a defective image could be repaired by determining the threshold of the residual image through the Otsu algorithm, and the difference between the input image and the repaired image will be compared to obtain the defect area. Finally, the experiment for the model validation is conducted on the complex surface image of the engine cylinder head. The result shows that the improved Generative Adversarial Networks is effective in both the image restoration and defect identification process.

Evaluation of an automatic method for detection of defects in linear and curvilinear ultrasound transducers.

The high incidence of defective ultrasound transducers in clinical practice has been shown in several studies. Recently, a novel method using only stored images for automatic detection of defective transducers was presented. The method makes it possible to remotely monitor many transducers at the same time and send a notification when a defective transducer is found. The purpose of the present study was to evaluate the novel method and assess how well it performs when compared to an established method as reference. To evaluate the novel method, in-air images were collected from 81 transducers in radiologic departments in nine hospitals. Two observers assessed the in-air images and marked the defects. Receiver operating characteristic (ROC)- and alternative free response receiver operating characteristic (AFROC)-curves and their figures of merit (FOM) were calculated for the novel method, using marked defects in the in-air images as reference truth. The area under the ROC curve was 0.88 (SD 0.06), and the AFROC FOM was 0.71 (SE 0.07). The result shows that the novel method has a good agreement with the in-air method for detecting defects in ultrasound systems. This indicates that the novel method could be a complement to the normal quality control for early, and automatic detection of defects.

Quantify point defects in monolayer tungsten diselenide

Point defects may significantly influence the electrical and optoelectronic properties of two-dimensional (2D) tungsten diselenide (WSe2), while precise information about point defects distribution (e.g. species and concentration) in monolayer (ML-) WSe2 are hard to obtain. In this letter, we tried to partly fill this knowledge gap via performing quantitative and statistical analysis of intrinsic point defects in WSe2 monolayers prepared by three so-called main-stream approaches i.e. mechanical exfoliation (ME), chemical vapor deposition (CVD), and molecular beam epitaxy (MBE), which are promising for providing high-quality samples. Via a conjunction of statistic atomic-resolution annular dark-field scanning transmission electron microscopy imaging, software-based automated defect identification and counting, together with image simulations, defect species and concentrations were quantitatively determined. Seven types of intrinsic point defects were identified in ML-WSe2 and the most dominant one is selenium mono-vacancy (VSe) (corresponding to one Se atom missing), irrespective of the synthetic route and growth conditions. Exact contents and diversity of point defects depend on the specific preparation method: CVD grown ML-WSe2 is the most defective (for example, the density of VSe reaches 1.48% in atomic ratio), followed by ME (∼0.85 at% for VSe) and MBE grown samples (∼0.49 at% for VSe). Our results, though still with limited sampling, provide preliminary quantitative information of point defects in ML-WSe2, which can serve as a reference to achieve the precisely controlled large-scale sample growth and establish the structure-property relationship of 2D transition-metal dichalcogenides materials.

Recognition of Scratches and Abrasions on Metal Surfaces Using a Classifier Based on a Convolutional Neural Network

Classification of steel surface defects in steel industry is essential for their detection and also fundamental for the analysis of causes that lead to damages. Timely detection of defects allows to reduce the frequency of their appearance in the final product. This paper considers the classifiers for the recognition of scratches, scrapes and abrasions on metal surfaces. Classifiers are based on the ResNet50 and ResNet152 deep residual neural network architecture. The proposed technique supports the recognition of defects in images and does this with high accuracy. The binary accuracy of the classification based on the test data is 97.14%. The influence of a number of training conditions on the accuracy metrics of the model have been studied. The augmentation conditions have been figured out to make the greatest contribution to improving the accuracy during training. The peculiarities of damages that cause difficulties in their recognition have been studied. The fields of neuron activation have been investigated in the convolutional layers of the model. Feature maps which developed in this case have been found to correspond to the location of the objects of interest. Erroneous cases of the classifier application have been considered. The peculiarities of damages that cause difficulties in their recognition have been studied.

A Novel Brain MRI Image Segmentation Method Using an Improved Multi-View Fuzzy c-Means Clustering Algorithm

Background: The brain magnetic resonance imaging (MRI) image segmentation method mainly refers to the division of brain tissue, which can be divided into tissue parts such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The segmentation results can provide a basis for medical image registration, 3D reconstruction, and visualization. Generally, MRI images have defects such as partial volume effects, uneven grayscale, and noise. Therefore, in practical applications, the segmentation of brain MRI images has difficulty obtaining high accuracy.Materials and Methods: The fuzzy clustering algorithm establishes the expression of the uncertainty of the sample category and can describe the ambiguity brought by the partial volume effect to the brain MRI image, so it is very suitable for brain MRI image segmentation (B-MRI-IS). The classic fuzzy c-means (FCM) algorithm is extremely sensitive to noise and offset fields. If the algorithm is used directly to segment the brain MRI image, the ideal segmentation result cannot be obtained. Accordingly, considering the defects of MRI medical images, this study uses an improved multiview FCM clustering algorithm (IMV-FCM) to improve the algorithm’s segmentation accuracy of brain images. IMV-FCM uses a view weight adaptive learning mechanism so that each view obtains the optimal weight according to its cluster contribution. The final division result is obtained through the view ensemble method. Under the view weight adaptive learning mechanism, the coordination between various views is more flexible, and each view can be adaptively learned to achieve better clustering effects.Results: The segmentation results of a large number of brain MRI images show that IMV-FCM has better segmentation performance and can accurately segment brain tissue. Compared with several related clustering algorithms, the IMV-FCM algorithm has better adaptability and better clustering performance.

Detection algorithm of defects on polyethylene gas pipe using image recognition

Aiming at the defects in the polyethylene (PE) gas pipeline, a defect detection algorithm based on image recognition is proposed. Firstly, the collected image is preliminarily screened to obtain the image with defects. Gamma correction algorithm is used to enhance the image, and then dual filtering is used to eliminate the interference of noise on image recognition. Then the defect edge is extracted by the improved traditional Sobel edge detection algorithm. Image defects are segmented by adaptive threshold method, and defect contours are effectively extracted through an open operation. Finally, the defect feature parameters are extracted and inputted into an optimized support vector machine for defect recognition. The experimental results show that the detection algorithm in this paper can effectively improve the observability of the image. Through the trained support vector machine, it can effectively identify the open joint, crack, and deformation defects. The recognition rate of pipeline image defects reached 96.3%.

Review on Buildings Health Monitoring System by Using Hybrid Data Optimization Based on Machine Learning Algorithm

The detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. The manual process of crack detection is pains takingly time-consuming and suffers from subjective judgments of inspectors. This study establishes an intelligent model based on image processing techniques for automatic crack recognition and analyses. In the new model, a gray intensity adjustment method, called Min-Max Gray Level Discrimination (M2GLD), is proposed to preprocess the image thresholded by the Otsu method. The goal of this gray intensity adjustment method is to meliorate the accuracy of the crack detection results. Experimental results point out that the integration of M2GLD and the Otsu method, followed by other shape analysis algorithms, can successfully detect crack defects in digital images. Therefore, the constructed model can be a useful tool for building management agencies and construction engineers in the task of structure maintenance.

Edge Defect Detection of Network Image by the Application of Modal Symmetry

In order to improve the accuracy of detection the image defect, a method to detect the edge defect based on modal symmetry algorithm was put forward. The improved PCNN was used to deal with the salt-pepper noise and Gaussian noise in image. On this basis, the semantic learning and annotation of image features were achieved. At first, the corresponding features were extracted from the original image. And then, the semantics were learned by combining the extracted features and the manually labeled library. Combined with the semantic annotation of image, the modal symmetry algorithm was adopted to linearly subtract the data collected by two centrosymmetric sampling points and thus to get the mean value. The asymmetric modal information of the whole image was obtained. Thus, the asymmetric modal could be extracted from the symmetrical modal. Due to the high amplitude of asymmetrical modal signal in defect location. Finally, the defect identification for various locations in image was completed by judging whether the amplitude of asymmetrical modal at the defect location had a sudden change. Following conclusions can be drawn from experimental results. The proposed method has excellent performance in image processing. Meanwhile, this method has high detection accuracy and practicability.