In the aerospace industry, the Automated Fiber Placement process is an established method for producing composite parts Nowadays the required visual inspection, subsequent to this process, typically takes up to 50% of the total manufacturing time and the inspection quality strongly depends on the inspector A Deep Learning based classification of manufacturing defects is a possibility to improve the process efficiency and accuracy However, these techniques require several hundreds or thousands of training data samples Acquiring this huge amount of data is difficult and time consuming in a real world manufacturing process Thus, an approach for augmenting a smaller number of defect images for the training of a neural network classifier is presented Five traditional methods and eight deep learning approaches are theoretically assessed according to the literature The selected conditional Deep Convolutional Generative Adversarial Network and Geometrical Transformation techniques are investigated in detail, with regard to the diversity and realism of the synthetic images Between 22 and 166 laser line scan sensor images per defect class from six common fiber placement inspection cases are utilised for tests The GAN-Train GAN-Test method was applied for the validation The studies demonstrated that a conditional Deep Convolutional Generative Adversarial Network combined with a previous Geometrical Transformation is well suited to generate a large realistic data set from less than 50 actual input images The presented network architecture and the associated training weights can serve as a basis for applying the demonstrated approach to other fibre layup inspection images

/pdf/synthetic-image-data-augmentation-for-fibre-layup-inspection-43m8aylzjs.pdf

Synthetic image data augmentation for fibre layup inspection processes: Techniques to enhance the data set

Computed tomography (CT) is widely used in medical diagnoses due to its ability to non-invasively detect the internal structures of the human body. However, CT scans with normal radiation doses can cause irreversible damage to patients. The radiation exposure is reduced with low-dose CT (LDCT), although considerable speckle noise and streak artifacts in CT images and even structural deformation may result, significantly undermining its diagnostic capability.This paper proposes a multistage network framework which gradually divides the entire process into 2-staged sub-networks to complete the task of image reconstruction. Specifically, a dilated residual convolutional neural network (DRCNN) was used to denoise the LDCT image. Then, the learned context information was combined with the channel attention subnet, which retains local information, to preserve the structural details and features of the image and textural information. To obtain recognizable characteristic details, we introduced a novel self-calibration module (SCM) between the 2 stages to reweight the local features, which realizes the complementation of information at different stages while refining feature information. In addition, we also designed an autoencoder neural network, using a self-supervised learning scheme to train a perceptual loss neural network specifically for CT images.We evaluated the diagnostic quality of the results and performed ablation experiments on the loss function and network structure modules to verify each module's effectiveness in the network. Our proposed network architecture obtained high peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and visual information fidelity (VIF) values in terms of quantitative evaluation. In the analysis of qualitative results, our network structure maintained a better balance between eliminating image noise and preserving image details. Experimental results showed that our proposed network structure obtained better metrics and visual evaluation.This study proposed a new LDCT image reconstruction method by combining autoencoder perceptual loss networks with multistage convolutional neural networks (MSCNN). Experimental results showed that the newly proposed method has performance than other methods. 

Low-dose computed tomography image reconstruction via a multistage convolutional neural network with autoencoder perceptual loss network

Super-resolution is one of the frequently investigated methods of image processing. The quality of the results is a constant problem in the methods used to obtain high resolution images. Interpolation-based methods have blurry output problems, while non-interpolation methods require a lot of training data and high computing power. In this paper, we present a supervised generative adversarial network system that accurately generates high resolution images from a low resolution input while maintaining pathological invariance. The proposed solution is optimized for small sets of input data. Compared to existing models, our network also provides faster learning. Another advantage of our approach is its versatility for various types of medical imaging methods. We used peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as the output image quality evaluation method. The results of our test show an improvement of 5.76% compared to optimizer Adam used in the original paper [10]. For faster training of the neural network model, calculations on the graphic card with the CUDA architecture were used.

Medical Image Data Upscaling with Generative Adversarial Networks

This study constructs a multimodal graph convolutional network model, conducts an in-depth study on image super-resolution relationship extraction and reconstruction methods, and constructs a model of image super-resolution relationship extraction and reconstruction methods based on multimodal graph convolutional networks. In this study, we study the domain adaptation model algorithm based on chart convolutional networks, which constructs a global relevance graph based on all samples using pre-extracted features and performs distribution approximation of sample features in two domains using a diagram convolutional neural network with maximum mean difference loss; with this approach, the model effectively preserves the structural information among the samples. In this study, several comparison experiments are designed based on the COCO and VG datasets; the image space information-based and knowledge graph-based target detection and recognition models substantially improve recognition performance over the baseline model. The super-pixel-based target detection and recognition model can also effectively reduce the number of floating-point operations and the complexity of the model. In this study, we propose a multiscale GAN-based image super-resolution reconstruction algorithm. Aiming at the problems of detail loss or blurring in the reconstruction of detail-rich images by SRGAN, it integrates the idea of the Laplace pyramid to complete the task of multiscale reconstruction of images through staged reconstruction. It incorporates the concept of a discriminative network with patch GAN to effectively improve the recovery effect of graph details and improve the reconstruction quality of images. Using Set5, Set14, BSD100, and Urban100 datasets as test sets, experimental analysis is conducted from objective and subjective evaluation metrics to effectively validate the performance of the improved algorithm proposed in this study.

Research on Super-Resolution Relationship Extraction and Reconstruction Methods for Images Based on Multimodal Graph Convolutional Networks

Abstract Multi modal MRI provides complementary and clinically relevant information from the image to understand the condition of the tissue and to characterize various complex diseases. But imaging artifacts influence the determination of relevant inforamtion from brain metastatis which is difficult to obtain adequate number of modalities from same study subject because of the under optimized study plan. However quantitative analysis becomes mandatory for in-depth understanding of the disease. Existing works could not exploit and maintain texture details from the scanners. In this research work, it is designed a multi label activated gradients for GAN (MLAG GAN) to enrich the corresponding reconstruction images with huge information. The detailed study trained the developed model by permitting the gradient flow from multiple gradients to a single generator at multiple labels thereby addressing the prevailing limitaiton of GAN. This system could exploit multi label neural transfer that enable to grasp more number of semantic and lesion related priors from the reference images. It is also validated the proposed system on Flair T1 and Flair T2 from Brats 18 dataset that depicts superior performance on the quality of image generation when compared to the state of art methods. The multi label GAN regenerates more high resolution structure and texture information and the wide range of qualitative and quantitative comparative experiments with the state of art methods proves the effectiveness of the proposed system in terms of L1, MSE (Mean square error), PSNR (Peak to signal ratio),SSIM (Structure similarity index measure), and Inception Score( IS). 

/pdf/medical-image-to-image-translation-using-multi-label-2jo42lxs.pdf

Medical Image to Image Translation using Multi Label Activated Gradients for General Adversial Networks ((MLAG GAN))

In the field of medical image processing represented by magnetic resonance imaging (MRI), synthesizing the complementary target contrast of the target patient from the existing contrast has obvious medical significance for assisting doctors in making clinical diagnoses. To satisfy the image translation problem between different MRI contrasts (T1 and T2), a generative adversarial network is proposed that works in an end-to-end manner at image level. The low-frequency and high-frequency information of the image is preserved by using multi-stage optimization learning aided by adversarial loss, the loss of perceptual consistency and the loss of cyclic consistency, as it results in preserving the same contrast anatomical structure of the source domain supervisely when the perceptual pixel distribution of the target contrast is learned perfectly. To integrate different penalties (L1 and L2) organically, adaptive weights are set for the error sensitivity of the penalty function in the present total loss function, the aim being to achieve adaptive optimization of each stage of generating high-resolution images. In addition, a new net structure called multi-skip connection residual net is proposed to refine medical image details step by step with multi-stage optimization. Compared with the existing technology, the present method is more advanced. The contrast conversion of T1 and T2 in MRI is validated, which can help to shorten the imaging time, improve the imaging quality, and effectively assist doctors with diagnoses.

Image synthesis in contrast MRI based on super resolution reconstruction with multi-refinement cycle-consistent generative adversarial networks

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Image synthesis in contrast MRI based on super resolution reconstruction with multi-refinement cycle-consistent generative adversarial networks