scholarly journals Image super-resolution reconstruction for secure data transmission in Internet of Things environment

2021 ◽  
Vol 18 (5) ◽  
pp. 6652-6671
Author(s):  
Hongan Li ◽  
◽  
Qiaoxue Zheng ◽  
Wenjing Yan ◽  
Ruolin Tao ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Internet Of Things ◽  
Network Model ◽  
Data Transmission ◽  
Super Resolution ◽  
Reconstruction Method ◽  
Secure Data ◽  
Secure Data Transmission ◽  
Image Super Resolution

<abstract><p>The image super-resolution reconstruction method can improve the image quality in the Internet of Things (IoT). It improves the data transmission efficiency, and is of great significance to data transmission encryption. Aiming at the problem of low image quality in image super-resolution using neural networks, a self-attention-based image reconstruction method is proposed for secure data transmission in IoT environment. The network model is improved, and the residual network structure and sub-pixel convolution are used to extract the feature of the image. The self-attention module is used extract detailed information in the image. Using generative confrontation method and image feature perception method to improve the image reconstruction effect. The experimental results on the public data set show that the improved network model improves the quality of the reconstructed image and can effectively restore the details of the image.</p></abstract>

scholarly journals Comparative Study and Design Light Weight Data Security System for Secure Data Transmission in Internet of Things

2021 ◽  
Vol 9 (3) ◽  
pp. 28-32
Author(s):  
Abha Jadaun ◽  
Satish Kumar Alaria ◽  
Yashika Saini
Keyword(s):  
Internet Of Things ◽  
Data Hiding ◽  
Data Transmission ◽  
Reversible Data Hiding ◽  
Secure Data ◽  
Secure Data Transmission ◽  
Symmetric Key ◽  
Symmetric Key Cryptography ◽  
Secured Data ◽  
Social Importance

Internet of things is shortened as IoT. Today IoT is a key and abrogating subject of the specialized and social importance. Results of buyers, things and vehicles, industry based and fundamental segments, sensors, and other everyday items are converged with network of internet and the solid information abilities which guarantee to change the sort in which we work and live. The proposed work demonstrates the implementation of symmetric key lightweight algorithm for secured data transmission of images and text using image encryption system as well as reversible data hiding system. In this paper, implemented symmetric key cryptography for various formats of images, as well as real time image acquisition system has been designed in the form of graphical user interface. Reversible data hiding system has also been designed for secure data transmission system.

scholarly journals An Image Super-Resolution Reconstruction Method with Single Frame Character Based on Wavelet Neural Network in Internet of Things

2020 ◽  
Author(s):  
Ling-li Guo ◽  
Marcin Woźniak
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Retention Rate ◽  
Image Acquisition ◽  
Super Resolution ◽  
Wavelet Neural Network ◽  
Reconstruction Method ◽  
Resolution Image ◽  
Single Frame ◽  
Image Super Resolution

AbstractThe application of the traditional single frame character image super-resolution reconstruction method has some problems, such as noise can not be removed completely and anti-interference performance is poor. A new method for the super-resolution reconstruction of single frame character image based on wavelet neural network is proposed. The structure and interface of image acquisition unit of solid state image sensor are designed. Combined with pinhole imaging model and camera self-calibration, image acquisition of Internet of Things is completed. An image degradation model was established to simulate the degradation process of ideal high-resolution image to low-resolution image. Wavelet threshold denoising method is used to remove the noise in a single frame character image and improve the anti-interference performance of the method. The wavelet neural network reflection model is used to reconstruct the single frame feature image and improve the resolution of the image. The experimental results show that the blur degree of the reconstructed image is always less than 5%. In the whole experiment, the accuracy of this method can be maintained at 80% ~ 90%. The image detail retention rate of the research method is relatively stable. With the increase of the number of experimental images, the retention rate of image details remains between 80% and 95%, indicating that the method is effective in practical application.

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