High-Resolution Image Inpainting Based on Multi-Scale Neural Network

Sun, Tingzhu; Fang, Weidong; Chen, Wei; Yao, Yanxin; Bi, Fangming; Wu, Baolei

doi:10.3390/electronics8111370

Cited by 16 publications

(9 citation statements)

References 29 publications

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“…In the experiments, COCO and VOC datasets which included 135,414 images as training data and 200 images as testing data were selected to evaluate the proposed method. The results show that the PSNR and SSIM of the proposed method were higher than the PSNR and SSIM of other methods [54].…”

Section: Measurement and Denoising Techniquesmentioning

confidence: 77%

“…) to analyze and denoise measured data in a variety of applications and services (e.g., wind speed prediction, air quality prediction, underground mine applications, neural audio caption, etc.). Several practical experiments were given in these articles, and the results indicated that the performance of the improved deep learning methods could be higher than the performance of conventional machine learning methods [43][44][45][46][47][48][49][50][51][52][53][54][55][56].…”

Section: Discussionmentioning

confidence: 99%

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Deep Learning Applications with Practical Measured Results in Electronics Industries

et al. 2020

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This editorial introduces the Special Issue, entitled “Deep Learning Applications with Practical Measured Results in Electronics Industries”, of Electronics. Topics covered in this issue include four main parts: (I) environmental information analyses and predictions, (II) unmanned aerial vehicle (UAV) and object tracking applications, (III) measurement and denoising techniques, and (IV) recommendation systems and education systems. Four papers on environmental information analyses and predictions are as follows: (1) “A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence” by Panapakidis et al.; (2) “Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting” by Wan et al.; (3) “Modeling and Analysis of Adaptive Temperature Compensation for Humidity Sensors” by Xu et al.; (4) “An Image Compression Method for Video Surveillance System in Underground Mines Based on Residual Networks and Discrete Wavelet Transform” by Zhang et al. Three papers on UAV and object tracking applications are as follows: (1) “Trajectory Planning Algorithm of UAV Based on System Positioning Accuracy Constraints” by Zhou et al.; (2) “OTL-Classifier: Towards Imaging Processing for Future Unmanned Overhead Transmission Line Maintenance” by Zhang et al.; (3) “Model Update Strategies about Object Tracking: A State of the Art Review” by Wang et al. Five papers on measurement and denoising techniques are as follows: (1) “Characterization and Correction of the Geometric Errors in Using Confocal Microscope for Extended Topography Measurement. Part I: Models, Algorithms Development and Validation” by Wang et al.; (2) “Characterization and Correction of the Geometric Errors Using a Confocal Microscope for Extended Topography Measurement, Part II: Experimental Study and Uncertainty Evaluation” by Wang et al.; (3) “Deep Transfer HSI Classification Method Based on Information Measure and Optimal Neighborhood Noise Reduction” by Lin et al.; (4) “Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets” by Chang et al.; (5) “High-Resolution Image Inpainting Based on Multi-Scale Neural Network” by Sun et al. Two papers on recommendation systems and education systems are as follows: (1) “Deep Learning-Enhanced Framework for Performance Evaluation of a Recommending Interface with Varied Recommendation Position and Intensity Based on Eye-Tracking Equipment Data Processing” by Sulikowski et al. and (2) “Generative Adversarial Network Based Neural Audio Caption Model for Oral Evaluation” by Zhang et al.

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Section: Measurement and Denoising Techniquesmentioning

confidence: 77%

Section: Discussionmentioning

confidence: 99%

Deep Learning Applications with Practical Measured Results in Electronics Industries

et al. 2020

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“…However, the non-local module was originally designed for the task of classification and this operation is not sufficient to significantly improve the performance of our framework. In addition to using the attention mechanism to obtain non-local information, a pre-trained model of the VGG network [ 22 ] has been wildly adopted to extract non-local features by calculating style loss. The essence of style loss is learning the relationship of existed and unknown regions by using the Gram-matrix to calculate the pixel relevance.…”

Section: Related Workmentioning

confidence: 99%

Non-Local and Multi-Scale Mechanisms for Image Inpainting

He¹,

Yin²

2021

Sensors

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Recently, deep learning-based techniques have shown great power in image inpainting especially dealing with squared holes. However, they fail to generate plausible results inside the missing regions for irregular and large holes as there is a lack of understanding between missing regions and existing counterparts. To overcome this limitation, we combine two non-local mechanisms including a contextual attention module (CAM) and an implicit diversified Markov random fields (ID-MRF) loss with a multi-scale architecture which uses several dense fusion blocks (DFB) based on the dense combination of dilated convolution to guide the generative network to restore discontinuous and continuous large masked areas. To prevent color discrepancies and grid-like artifacts, we apply the ID-MRF loss to improve the visual appearance by comparing similarities of long-distance feature patches. To further capture the long-term relationship of different regions in large missing regions, we introduce the CAM. Although CAM has the ability to create plausible results via reconstructing refined features, it depends on initial predicted results. Hence, we employ the DFB to obtain larger and more effective receptive fields, which benefits to predict more precise and fine-grained information for CAM. Extensive experiments on two widely-used datasets demonstrate that our proposed framework significantly outperforms the state-of-the-art approaches both in quantity and quality.

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“…The ChestX-ray image of a patient needs to be read by a senior radiologist for at least 10 min to make a diagnosis and different doctors can make inconsistent diagnoses of the same ChestX-ray image, which means that the results are affected by the cognitive ability of the radiologist, subjective experience, fatigue and other factors [2]. Computer-aided diagnosis (CAD) can overcome the deficiencies of radiologists, make Recently, benefitting from deep learning techniques [6], computer vision [7] has had remarkable success in the fields of target detection [8], image classification [9,10] and image inpainting [11], for example. This notable progress has led to the development of many medical image processing applications, including disease classification [12], lesion detection or segmentation [13][14][15], registration [16], image annotation [17,18] as well as other examples [19].…”

Section: Introductionmentioning

confidence: 99%

Fusion High-Resolution Network for Diagnosing ChestX-ray Images

Huang

Lin

et al. 2020

Electronics

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The application of deep convolutional neural networks (CNN) in the field of medical image processing has attracted extensive attention and demonstrated remarkable progress. An increasing number of deep learning methods have been devoted to classifying ChestX-ray (CXR) images, and most of the existing deep learning methods are based on classic pretrained models, trained by global ChestX-ray images. In this paper, we are interested in diagnosing ChestX-ray images using our proposed Fusion High-Resolution Network (FHRNet). The FHRNet concatenates the global average pooling layers of the global and local feature extractors—it consists of three branch convolutional neural networks and is fine-tuned for thorax disease classification. Compared with the results of other available methods, our experimental results showed that the proposed model yields a better disease classification performance for the ChestX-ray 14 dataset, according to the receiver operating characteristic curve and area-under-the-curve score. An ablation study further confirmed the effectiveness of the global and local branch networks in improving the classification accuracy of thorax diseases.

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High-Resolution Image Inpainting Based on Multi-Scale Neural Network

Cited by 16 publications

References 29 publications

Deep Learning Applications with Practical Measured Results in Electronics Industries

Deep Learning Applications with Practical Measured Results in Electronics Industries

Non-Local and Multi-Scale Mechanisms for Image Inpainting

Fusion High-Resolution Network for Diagnosing ChestX-ray Images

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