scholarly journals Robust Nonparametric Distribution Transfer with Exposure Correction for Image Neural Style Transfer

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5232
Author(s):  
Shuai Liu ◽  
Caixia Hong ◽  
Jing He ◽  
Zhiqiang Tian
Keyword(s):  
Dynamic Range ◽  
Color Difference ◽  
Industrial Applications ◽  
Correction Algorithm ◽  
Style Transfer ◽  
Qualitative And Quantitative ◽  
Academic Literature ◽  
Nonparametric Distribution ◽  
Quantitative Results ◽  
The Given

Image neural style transfer is a process of utilizing convolutional neural networks to render a content image based on a style image. The algorithm can compute a stylized image with original content from the given content image but a new style from the given style image. Style transfer has become a hot topic both in academic literature and industrial applications. The stylized results of current existing models are not ideal because of the color difference between two input images and the inconspicuous details of content image. To solve the problems, we propose two style transfer models based on robust nonparametric distribution transfer. The first model converts the color probability density function of the content image into that of the style image before style transfer. When the color dynamic range of the content image is smaller than that of style image, this model renders more reasonable spatial structure than the existing models. Then, an adaptive detail-enhanced exposure correction algorithm is proposed for underexposed images. Based this, the second model is proposed for the style transfer of underexposed content images. It can further improve the stylized results of underexposed images. Compared with popular methods, the proposed methods achieve the satisfactory qualitative and quantitative results.

scholarly journals UCAN: A Learning-based Model to Enhance Poorly Exposed Images

2020 ◽  
Author(s):  
Lucas R. V. Messias ◽  
Cristiano R. Steffens ◽  
Paulo L. J. Drews-Jr ◽  
Silvia S. C. Botelho
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Dynamic Range ◽  
Low Contrast ◽  
Qualitative And Quantitative ◽  
Fast Learning ◽  
Proposed Model ◽  
Quantitative Results ◽  
Critical Process ◽  
Poor Lighting

Image enhancement is a critical process in imagebased systems. In these systems, image quality is a crucial factor to achieve a good performance. Scenes with a dynamic range above the capability of the camera or poor lighting are challenging conditions, which usually result in low contrast images, and, with that, we can have the underexposure and/or overexposure problem. In this work, our aim is to restore illexposed images. For this purpose, we present UCAN, a small and fast learning-based model capable to restore and enhance poorly exposed images. The obtained results are evaluated using image quality indicators which show that the proposed network is able to improve images damaged by real and simulated exposure. Qualitative and quantitative results show that the proposed model outperforms the existing models for this objective.

scholarly journals Medical image processing with contextual style transfer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yin Xu ◽  
Yan Li ◽  
Byeong-Seok Shin
Keyword(s):  
Medical Image ◽  
Medical Image Processing ◽  
Image Synthesis ◽  
Industrial Applications ◽  
Generative Models ◽  
Context Aware ◽  
Training Set ◽  
Style Transfer ◽  
Segmentation Accuracy ◽  
Generative Methods

Abstract With recent advances in deep learning research, generative models have achieved great achievements and play an increasingly important role in current industrial applications. At the same time, technologies derived from generative methods are also under a wide discussion with researches, such as style transfer, image synthesis and so on. In this work, we treat generative methods as a possible solution to medical image augmentation. We proposed a context-aware generative framework, which can successfully change the gray scale of CT scans but almost without any semantic loss. By producing target images that with specific style / distribution, we greatly increased the robustness of segmentation model after adding generations into training set. Besides, we improved 2– 4% pixel segmentation accuracy over original U-NET in terms of spine segmentation. Lastly, we compared generations produced by networks when using different feature extractors (Vgg, ResNet and DenseNet) and made a detailed analysis on their performances over style transfer.

scholarly journals Affective communication: a mixed method investigation into COVID-19 outbreak communication using the Taiwanese government Facebook page

2021 ◽  
pp. 175797592110035
Author(s):  
Chia Yu Lien ◽  
Yun-Hsuan Wu
Keyword(s):  
Social Media ◽  
Mixed Method ◽  
Health Authority ◽  
Disease Outbreak ◽  
Government Agencies ◽  
Affective Communication ◽  
Frontline Workers ◽  
The Public ◽  
Qualitative And Quantitative ◽  
Quantitative Results

The COVID-19 outbreak has created an unprecedented challenge for governments to convey information to the public, and social media has become a critical method of COVID-19 communication in Taiwan. Objectives: This study examines a total of 1128 Facebook posts published by Taiwan’s principal health authority from December 1, 2019 to May 31, 2020. Methods: Using both qualitative and quantitative approaches, this study investigates strategies used by the Taiwan government to communicate the COVID-19 outbreak and public responses toward these strategies. Result: Novel uses of Facebook posts on outbreak communication were identified, including solidarity, reviews of actions, press conferences, and the use of animal and cartoon images. Quantitative results showed that the public responded significantly more frequently to messages generating positive affects, such as posts that reviewed government actions and public efforts; posts that expressed thanks, approval, or comradeship; and posts that paired text with photographs of frontline workers or cute animals. Conclusion: These results suggest that, amid a disease outbreak, the public not only look for updated situations and guidelines but also for affective affirmation from government agencies.

Multidirection Validation of a Finite Element 50th Percentile Male Hybrid III Anthropomorphic Test Device for Spaceflight Applications

2019 ◽  
Vol 141 (3) ◽  
Author(s):  
Derek A. Jones ◽  
James P. Gaewsky ◽  
Mona Saffarzadeh ◽  
Jacob B. Putnam ◽  
Ashley A. Weaver ◽  
...  
Keyword(s):  
Finite Element ◽  
Injury Risk ◽  
Fe Model ◽  
Fe Modeling ◽  
Test Device ◽  
Qualitative And Quantitative ◽  
Hybrid Iii ◽  
Physical Tests ◽  
Quantitative Results ◽  
Male Hybrid

The use of anthropomorphic test devices (ATDs) for calculating injury risk of occupants in spaceflight scenarios is crucial for ensuring the safety of crewmembers. Finite element (FE) modeling of ATDs reduces cost and time in the design process. The objective of this study was to validate a Hybrid III ATD FE model using a multidirection test matrix for future spaceflight configurations. Twenty-five Hybrid III physical tests were simulated using a 50th percentile male Hybrid III FE model. The sled acceleration pulses were approximately half-sine shaped, and can be described as a combination of peak acceleration and time to reach peak (rise time). The range of peak accelerations was 10–20 G, and the rise times were 30–110 ms. Test directions were frontal (−GX), rear (GX), vertical (GZ), and lateral (GY). Simulation responses were compared to physical tests using the correlation and analysis (CORA) method. Correlations were very good to excellent and the order of best average response by direction was −GX (0.916±0.054), GZ (0.841±0.117), GX (0.792±0.145), and finally GY (0.775±0.078). Qualitative and quantitative results demonstrated the model replicated the physical ATD well and can be used for future spaceflight configuration modeling and simulation.

scholarly journals Adaptive Image Enhancement Using Entropy-Based Subhistogram Equalization

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Liyun Zhuang ◽  
Yepeng Guan
Keyword(s):  
Image Enhancement ◽  
Dynamic Range ◽  
State Of The Art ◽  
Image Database ◽  
Input Image ◽  
Public Image ◽  
Contrast Enhanced ◽  
Quantitative Results ◽  
The Mean ◽  
Visual Assessments

A novel image enhancement approach called entropy-based adaptive subhistogram equalization (EASHE) is put forward in this paper. The proposed algorithm divides the histogram of input image into four segments based on the entropy value of the histogram, and the dynamic range of each subhistogram is adjusted. A novel algorithm to adjust the probability density function of the gray level is proposed, which can adaptively control the degree of image enhancement. Furthermore, the final contrast-enhanced image is obtained by equalizing each subhistogram independently. The proposed algorithm is compared with some state-of-the-art HE-based algorithms. The quantitative results for a public image database named CVG-UGR-Database are statistically analyzed. The quantitative and visual assessments show that the proposed algorithm outperforms most of the existing contrast-enhancement algorithms. The proposed method can make the contrast of image more effectively enhanced as well as the mean brightness and details well preserved.

Nonlinear Analysis of a Rigid Rotor on Magnetic Bearings

1995 ◽  
Author(s):  
C. Nataraj
Keyword(s):  
Nonlinear Analysis ◽  
Equations Of Motion ◽  
Forced Response ◽  
Magnetic Bearings ◽  
Rigid Rotor ◽  
Stability Criteria ◽  
Safe Operation ◽  
Qualitative And Quantitative ◽  
Quantitative Results ◽  
Nonlinear Equations Of Motion

A simple model of a rigid rotor supported on magnetic bearings is considered. A proportional control architecture is assumed, the nonlinear equations of motion are derived and some essential nondimensional parameters are identified. The free and forced response of the system is analyzed using techniques of nonlinear analysis. Both qualitative and quantitative results are obtained and stability criteria are derived for safe operation of the system.

scholarly journals Accurate Instance Segmentation in Pediatric Elbow Radiographs

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7966
Author(s):  
Dixiao Wei ◽  
Qiongshui Wu ◽  
Xianpei Wang ◽  
Meng Tian ◽  
Bowen Li
Keyword(s):  
Elbow Joint ◽  
Segmentation Method ◽  
Qualitative And Quantitative ◽  
Robust Segmentation ◽  
Local Contexts ◽  
Good Potential ◽  
Quantitative Results ◽  
Bounding Boxes ◽  
Global And Local ◽  
Instance Segmentation

Radiography is an essential basis for the diagnosis of fractures. For the pediatric elbow joint diagnosis, the doctor needs to diagnose abnormalities based on the location and shape of each bone, which is a great challenge for AI algorithms when interpreting radiographs. Bone instance segmentation is an effective upstream task for automatic radiograph interpretation. Pediatric elbow bone instance segmentation is a process by which each bone is extracted separately from radiography. However, the arbitrary directions and the overlapping of bones pose issues for bone instance segmentation. In this paper, we design a detection-segmentation pipeline to tackle these problems by using rotational bounding boxes to detect bones and proposing a robust segmentation method. The proposed pipeline mainly contains three parts: (i) We use Faster R-CNN-style architecture to detect and locate bones. (ii) We adopt the Oriented Bounding Box (OBB) to improve the localizing accuracy. (iii) We design the Global-Local Fusion Segmentation Network to combine the global and local contexts of the overlapped bones. To verify the effectiveness of our proposal, we conduct experiments on our self-constructed dataset that contains 1274 well-annotated pediatric elbow radiographs. The qualitative and quantitative results indicate that the network significantly improves the performance of bone extraction. Our methodology has good potential for applying deep learning in the radiography’s bone instance segmentation.

scholarly journals DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion

2020 ◽  
Author(s):  
Zixiang Zhao ◽  
Shuang Xu ◽  
Chunxia Zhang ◽  
Junmin Liu ◽  
Jiangshe Zhang ◽  
...  
Keyword(s):  
Image Fusion ◽  
Feature Maps ◽  
Visible Image ◽  
Qualitative And Quantitative ◽  
Fused Image ◽  
Fusion Images ◽  
Quantitative Results ◽  
Deep Image ◽  
Core Idea ◽  
Background Detail

Infrared and visible image fusion, a hot topic in the field of image processing, aims at obtaining fused images keeping the advantages of source images. This paper proposes a novel auto-encoder (AE) based fusion network. The core idea is that the encoder decomposes an image into background and detail feature maps with low- and high-frequency information, respectively, and that the decoder recovers the original image. To this end, the loss function makes the background/detail feature maps of source images similar/dissimilar. In the test phase, background and detail feature maps are respectively merged via a fusion module, and the fused image is recovered by the decoder. Qualitative and quantitative results illustrate that our method can generate fusion images containing highlighted targets and abundant detail texture information with strong reproducibility and meanwhile surpass state-of-the-art (SOTA) approaches.

scholarly journals The Development of Radio and 3G Based Telemetry System for the Remote Gas Accounting and Control Nodes

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1417-1421
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Industrial Applications ◽  
Telemetry System ◽  
Computing Device ◽  
Gas Leakage ◽  
Remote Environment ◽  
High Dynamic ◽  
And Control ◽  
Direct Management

In this article we have described the use of vortex and recently developed ultrasonic flowmeters with high dynamic range of 1 to 1500 for industrial applications. Its software and the software of corresponding computing device is able to avoid gas leakage, to minimize energy consumption and to save human resources while maintaining metrological data. Described is the low power consumption that makes it possible to use this ultrasonic flowmeter in hard remote environment without direct management for a period of several months. Shown is the new telemetry system that was developed to unite flowmeters in the severe conditions of the desert with power supply problems and low GPRS signal quality. Experiments held in Turkmenistan have shown that device indications didn’t drift and remained stable during the year, that is a great advantage in comparison to rotary and turbine flowmeters. Also described is the mobile ultrasonic calibration stand that uses the same physical principles and similar software. Outlined is the usage of modern wireless technologies to collect and transmit metrological data.

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