Deep Learning-Based Synthesized View Quality Enhancement with DIBR Distortion Mask Prediction Using Synthetic Images

Zhang, Huan; Cao, Jiangzhong; Zheng, Dongsheng; Yao, Ximei; Ling, Wing‐Kuen

doi:10.3390/s22218127

Cited by 4 publications

(2 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The random perturbation of angular space between points is presented by ∆θ i which follows a uniform distribution that has an upper and lower limit of 2π n − β 1 , and 2π n + β 1 , respectively. In addition, a Gaussian distribution is used for r i with a mean diameter of d * and variance of β 2 [57]. The size of the generated polygon can be controlled with d * .…”

Section: Random Polygon Generatormentioning

confidence: 99%

A finite element-convolutional neural network model (FE-CNN) for stress field analysis around arbitrary inclusions

Rezasefat,

Hogan

2023

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

This study presents a data-driven finite element-machine learning surrogate model for predicting the end-to-end full-field stress distribution and stress concentration around an arbitrary-shaped inclusion. This is important because the model’s capacity to handle large datasets, consider variations in size and shape, and accurately replicate stress fields makes it a valuable tool for studying how inclusion characteristics affect material performance. An automatized dataset generation method using finite element simulation is proposed, validated, and used for attaining a dataset with one thousand inclusion shapes motivated by experimental observations and their corresponding spatially-varying stress distributions. A U-Net-based convolutional neural network (CNN) is trained using the dataset, and its performance is evaluated through quantitative and qualitative comparisons. The dataset, consisting of these stress data arrays, is directly fed into the CNN model for training and evaluation. This approach bypasses the need for converting the stress data into image format, allowing for a more direct and efficient input representation for the CNN. The model was evaluated through a series of sensitivity analyses, focusing on the impact of dataset size and model resolution on accuracy and performance. The results demonstrated that increasing the dataset size significantly improved the model’s prediction accuracy, as indicated by the correlation values. Additionally, the investigation into the effect of model resolution revealed that higher resolutions led to better stress field predictions and reduced error. Overall, the surrogate model proved effective in accurately predicting the effective stress concentration in inclusions, showcasing its potential in practical applications requiring stress analysis such as structural engineering, material design, failure analysis, and multi-scale modeling.

show abstract

Section: Random Polygon Generatormentioning

confidence: 99%

A finite element-convolutional neural network model (FE-CNN) for stress field analysis around arbitrary inclusions

Rezasefat,

Hogan

2023

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…However, the training of CNNs requires many defect samples and necessitates additional manpower for defect labeling, which makes it difficult to import deep learning technology into actual production lines. Although many data augmentation methods have been proposed thus far [ 11 , 12 , 13 , 14 ], the application of CNNs in industrial testing remains limited.…”

Section: Introductionmentioning

confidence: 99%

Industrial Anomaly Detection with Skip Autoencoder and Deep Feature Extractor

Tang

Hsu²,

Huang

et al. 2022

Sensors

View full text Add to dashboard Cite

Over recent years, with the advances in image recognition technology for deep learning, researchers have devoted continued efforts toward importing anomaly detection technology into the production line of automatic optical detection. Although unsupervised learning helps overcome the high costs associated with labeling, the accuracy of anomaly detection still needs to be improved. Accordingly, this paper proposes a novel deep learning model for anomaly detection to overcome this bottleneck. Leveraging a powerful pre-trained feature extractor and the skip connection, the proposed method achieves better feature extraction and image reconstructing capabilities. Results reveal that the areas under the curve (AUC) for the proposed method are higher than those of previous anomaly detection models for 16 out of 17 categories. This indicates that the proposed method can realize the most appropriate adjustments to the needs of production lines in order to maximize economic benefits.

show abstract

A deep learning model, NAFNet, predicts adverse pathology and recurrence in prostate cancer using MRIs

Gu,

Liu,

Yang

et al. 2023

npj Precis. Onc.

View full text Add to dashboard Cite

We aimed to apply a potent deep learning network, NAFNet, to predict adverse pathology events and biochemical recurrence-free survival (bRFS) based on pre-treatment MRI imaging. 514 prostate cancer patients from six tertiary hospitals throughout China from 2017 and 2021 were included. A total of 367 patients from Fudan University Shanghai Cancer Center with whole-mount histopathology of radical prostatectomy specimens were assigned to the internal set, and cancer lesions were delineated with whole-mount pathology as the reference. The external test set included 147 patients with BCR data from five other institutes. The prediction model (NAFNet-classifier) and integrated nomogram (DL-nomogram) were constructed based on NAFNet. We then compared DL-nomogram with radiology score (PI-RADS), and clinical score (Cancer of the Prostate Risk Assessment score (CAPRA)). After training and validation in the internal set, ROC curves in the external test set showed that NAFNet-classifier alone outperformed ResNet50 in predicting adverse pathology. The DL-nomogram, including the NAFNet-classifier, clinical T stage and biopsy results, showed the highest AUC (0.915, 95% CI: 0.871–0.959) and accuracy (0.850) compared with the PI-RADS and CAPRA scores. Additionally, the DL-nomogram outperformed the CAPRA score with a higher C-index (0.732, P < 0.001) in predicting bRFS. Based on this newly-developed deep learning network, NAFNet, our DL-nomogram could accurately predict adverse pathology and poor prognosis, providing a potential AI tools in medical imaging risk stratification.

show abstract

Deep Learning-Based Synthesized View Quality Enhancement with DIBR Distortion Mask Prediction Using Synthetic Images

Cited by 4 publications

References 48 publications

A finite element-convolutional neural network model (FE-CNN) for stress field analysis around arbitrary inclusions

A finite element-convolutional neural network model (FE-CNN) for stress field analysis around arbitrary inclusions

Industrial Anomaly Detection with Skip Autoencoder and Deep Feature Extractor

A deep learning model, NAFNet, predicts adverse pathology and recurrence in prostate cancer using MRIs

Contact Info

Product

Resources

About