scholarly journals A Survey of Image Information Hiding Algorithms Based on Deep Learning

2018 ◽  
Vol 117 (3) ◽  
pp. 425-454 ◽  
Author(s):  
Ruohan Meng ◽  
Qi Cui ◽  
Chengsheng Yuan
Keyword(s):  
Deep Learning ◽  
Information Hiding ◽  
Image Information

scholarly journals Research on the Application of Artificial Intelligence Machine Learning Technology in Improving the Accuracy of Engineering Image Processing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042007
Author(s):  
Xiaowen Liu ◽  
Juncheng Lei
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Image Recognition ◽  
Recognition Performance ◽  
Color Space ◽  
Gaussian Model ◽  
Image Feature ◽  
Layer By Layer ◽  
Image Information

Abstract Image recognition technology mainly includes image feature extraction and classification recognition. Feature extraction is the key link, which determines whether the recognition performance is good or bad. Deep learning builds a model by building a hierarchical model structure like the human brain, extracting features layer by layer from the data. Applying deep learning to image recognition can further improve the accuracy of image recognition. Based on the idea of clustering, this article establishes a multi-mix Gaussian model for engineering image information in RGB color space through offline learning and expectation-maximization algorithms, to obtain a multi-mix cluster representation of engineering image information. Then use the sparse Gaussian machine learning model on the YCrCb color space to quickly learn the distribution of engineering images online, and design an engineering image recognizer based on multi-color space information.

scholarly journals Design of Commodity Settlement System Based on Deep Learning

2021 ◽  
Vol 2066 (1) ◽  
pp. 012007
Author(s):  
Mouli Liu
Keyword(s):  
Deep Learning ◽  
Smart Phone ◽  
Commodity Price ◽  
Unit Price ◽  
Qr Code ◽  
Settlement System ◽  
Image Information ◽  
Bar Code ◽  
System A ◽  
Upper Computer

Abstract In order to solve the problem that the salesmen need to scan the bar code of commodity price one by one in supermarket settlement system, a commodity settlement system is proposed. The system hardware includes smart phone, PC and LCD. The image information is obtained after the smart phone scans goods, then the acquired image is uploaded to the upper computer, which identifies commodity and obtains the unit price of goods through the image, finally, the payment QR code is displayed on the LCD screen. Without scanning the cods again at the checkout counter, the payment could be accomplished in a manner that saved time and manpower cost.

scholarly journals Unpaired Low-Dose CT Denoising Network Based on Cycle-Consistent Generative Adversarial Network with Prior Image Information

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Chao Tang ◽  
Jie Li ◽  
Linyuan Wang ◽  
Ziheng Li ◽  
Lingyun Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Radiation Dose ◽  
Prior Information ◽  
Low Dose ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Widespread Application ◽  
Image Information ◽  
Low Dose Ct ◽  
One To One

The widespread application of X-ray computed tomography (CT) in clinical diagnosis has led to increasing public concern regarding excessive radiation dose administered to patients. However, reducing the radiation dose will inevitably cause server noise and affect radiologists’ judgment and confidence. Hence, progressive low-dose CT (LDCT) image reconstruction methods must be developed to improve image quality. Over the past two years, deep learning-based approaches have shown impressive performance in noise reduction for LDCT images. Most existing deep learning-based approaches usually require the paired training dataset which the LDCT images correspond to the normal-dose CT (NDCT) images one-to-one, but the acquisition of well-paired datasets requires multiple scans, resulting the increase of radiation dose. Therefore, well-paired datasets are not readily available. To resolve this problem, this paper proposes an unpaired LDCT image denoising network based on cycle generative adversarial networks (CycleGAN) with prior image information which does not require a one-to-one training dataset. In this method, cyclic loss, an important trick in unpaired image-to-image translation, promises to map the distribution from LDCT to NDCT by using unpaired training data. Furthermore, to guarantee the accurate correspondence of the image content between the output and NDCT, the prior information obtained from the result preprocessed using the LDCT image is integrated into the network to supervise the generation of content. Given the map of distribution through the cyclic loss and the supervision of content through the prior image loss, our proposed method can not only reduce the image noise but also retain critical information. Real-data experiments were carried out to test the performance of the proposed method. The peak signal-to-noise ratio (PSNR) improves by more than 3 dB, and the structural similarity (SSIM) increases when compared with the original CycleGAN without prior information. The real LDCT data experiment demonstrates the superiority of the proposed method according to both visual inspection and quantitative evaluation.

scholarly journals Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography

2021 ◽  
Vol 94 (1117) ◽  
pp. 20200677
Author(s):  
Andrea Steuwe ◽  
Marie Weber ◽  
Oliver Thomas Bethge ◽  
Christin Rademacher ◽  
Matthias Boschheidgen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Dose Reduction ◽  
Low Dose ◽  
Ct Images ◽  
Image Noise ◽  
The Novel ◽  
Subjective Image Quality ◽  
Image Information ◽  
Noise Ratio

Objectives: Modern reconstruction and post-processing software aims at reducing image noise in CT images, potentially allowing for a reduction of the employed radiation exposure. This study aimed at assessing the influence of a novel deep-learning based software on the subjective and objective image quality compared to two traditional methods [filtered back-projection (FBP), iterative reconstruction (IR)]. Methods: In this institutional review board-approved retrospective study, abdominal low-dose CT images of 27 patients (mean age 38 ± 12 years, volumetric CT dose index 2.9 ± 1.8 mGy) were reconstructed with IR, FBP and, furthermore, post-processed using a novel software. For the three reconstructions, qualitative and quantitative image quality was evaluated by means of CT numbers, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in six different ROIs. Additionally, the reconstructions were compared using SNR, peak SNR, root mean square error and mean absolute error to assess structural differences. Results: On average, CT numbers varied within 1 Hounsfield unit (HU) for the three assessed methods in the assessed ROIs. In soft tissue, image noise was up to 42% lower compared to FBP and up to 27% lower to IR when applying the novel software. Consequently, SNR and CNR were highest with the novel software. For both IR and the novel software, subjective image quality was equal but higher than the image quality of FBP-images. Conclusion: The assessed software reduces image noise while maintaining image information, even in comparison to IR, allowing for a potential dose reduction of approximately 20% in abdominal CT imaging. Advances in knowledge: The assessed software reduces image noise by up to 27% compared to IR and 48% compared to FBP while maintaining the image information. The reduced image noise allows for a potential dose reduction of approximately 20% in abdominal imaging.

Local Feature Based Geometric-Resistant Image Information Hiding

2010 ◽  
Vol 2 (2) ◽  
pp. 68-77 ◽  
Author(s):  
Xinbo Gao ◽  
Cheng Deng ◽  
Xuelong Li ◽  
Dacheng Tao
Keyword(s):  
Information Hiding ◽  
Local Feature ◽  
Image Information ◽  
Feature Based

A Coverless Image Information Hiding Algorithm Based on Fractal Theory

2020 ◽  
Vol 30 (04) ◽  
pp. 2050062
Author(s):  
Xiang Zhang ◽  
Fei Peng ◽  
Zixing Lin ◽  
Min Long
Keyword(s):  
Information Hiding ◽  
Fractal Theory ◽  
Covert Communication ◽  
Generation Process ◽  
Image Generation ◽  
Generation Algorithm ◽  
Image Information ◽  
Fractal Image ◽  
Coverless Information Hiding ◽  
The Relationship

To improve the robustness and imperceptibility of the existing coverless image information hiding, a generative coverless image information hiding algorithm based on fractal theory is proposed in this paper. Firstly, four fractal image generation methods are analyzed, and the relationship between the coverless information hiding and these methods is discussed. Secondly, based on the fractal image generation algorithm, secret information is hidden by controlling pixel rendering during the generation process. The robustness, imperceptibility, and capability of resisting steganalysis are balanced by adjusting the rendering distance. As it directly generates stego images, this can resist the detection of most existing steganalysis methods. Meanwhile, different capacities can be achieved by adjusting the size of the generated image. Experimental results and analysis show that the proposed scheme can effectively resist steganalysis and has good robustness against various image attacks. Furthermore, it can achieve large capacity, and it has broad prospects for covert communication.

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