Reliable Data Aggregation in Internet of ViSAR Vehicles Using Chained Dual-Phase Adaptive Interpolation and Data Embedding

Khosravi, Mohammad R.; Samadi, Sadegh

doi:10.1109/jiot.2019.2952284

Cited by 41 publications

(18 citation statements)

References 26 publications

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“…The raindrop removal method proposed in this paper can remove most of the raindrops in the image and repair the background image of the raindrop covered part. It can be seen from the attention map that the location and size of [25][26][27] and the method proposed in this paper to remove raindrops, and calculate the PSNR value and SSIM value of the two methods to obtain the image; the results are shown in Table 1. It can be seen from Table 1 that the PSNR and SSIM of the image obtained by the method proposed in this paper are higher than those of Yang and Fu, which indicates that the similarity between the raindrop image obtained by the method proposed in this paper and the original clear background image is higher, which proves that the effect of the raindrop method based on the generated antinetwork is better than that of Yang and Fu.…”

Section: Comparison Of Effect Pictures Of Raindropmentioning

confidence: 99%

Generative Adversarial Network for Image Raindrop Removal of Transmission Line Based on Unmanned Aerial Vehicle Inspection

Gao

et al. 2021

Wireless Communications and Mobile Computing

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In the process of UAV line inspection, there may be raindrops on the camera lens. Raindrops have a serious impact on the details of the image, reducing the identification of the target transmission equipment in the image, reducing the accuracy of the target detection algorithm, and hindering the practicability of UAV line inspection technology in cyber-physical energy systems. In this paper, the principle of raindrop image formation is studied, and a method of raindrop removal based on generation countermeasure network is proposed. In this method, the attention recurrent network is used to generate the raindrop attention map, and the context code decoder is used to generate the raindrop image. The experimental results show that the proposed method can remove the raindrops in the image and repair the background image of raindrop coverage area and can generate a higher quality raindrop removal image than the traditional method.

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Section: Comparison Of Effect Pictures Of Raindropmentioning

confidence: 99%

Generative Adversarial Network for Image Raindrop Removal of Transmission Line Based on Unmanned Aerial Vehicle Inspection

Gao

et al. 2021

Wireless Communications and Mobile Computing

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“…Digital watermarking methods are mainly used in healthcare [25], remote sensing [22] and industrial [18,24] application areas for the purpose of ownership identification [30], copyright protection [49], fingerprinting [32,33,36], monitoring [10,19,20] and forgery detection [12]. With respect to the watermark embedding domain digital watermarking methods like steganography [26,27,29,35] can be generally classified as spatial domain methods and transform domain methods such as discrete Fourier transform [34], discrete cosine transform [1,3,23], discrete wavelet transform [2,17,21,41,42].…”

Section: Related Workmentioning

confidence: 99%

A novel triple recovery information embedding approach for self-embedded digital image watermarking

Gül

Öztürk

2020

Multimed Tools Appl

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Image tamper detection and recovery has become an important issue in recent years. In order to detect and recover high tampering rated images, this paper presents a novel selfembedded fragile watermarking method with triple recovery information embedding approach. In this proposed method host image is divided into sixteen main blocks. Four partner blocks are selected from main blocks as a partner group using Look-Up table which is constructed for recovery against high tampering rated images. Each partner block is divided into 4 × 4 blocks. Recovery data of each partner block is generated from the mean values of the 4 × 4 blocks. Then, triple recovery information for each partner block is constructed by combining recovery data of the three other partner blocks. After the generation of the recovery information, each partner block is divided into 16 × 16 blocks. Then, these blocks are divided into four 8 × 8 sub-blocks. Recovery bits for each partner block are embedded into the first and second least significant bits (LSBs) of the first three sub-blocks. It means that triple copies of recovery information for each partner block are embedded into three other blocks in the image, which provides triple chance for recovery of the tampered areas. On the other hand, authentication information is generated from the pixels of the 16 × 16 recovery information embedded image blocks by using MD5 hash function. This authentication information is embedded into the quarter part of each 16 × 16 block by using LSB substitution. The performance of the proposed method has been demonstrated by applying different size of cropping attacks to the different areas of the watermarked images. Experimental results show that the proposed method satisfactory recovers up to 75% tampering rated images.

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“…[4] is one of the oldest and most leading research about Video-SAR which was published in 2010. Video-SAR has had a fast progress in the last decade in terms of academic attention and development in aspects of signal and data processing [5][6][7][8][9][10][11][12][13][14] . In this paper, the aim is to review the main objectives behind the Video-SAR systems in order to make a comprehensive systematic review with emphasis on data processing.…”

Section: Introductionmentioning

confidence: 99%

Mobile multimedia computing in cyber-physical surveillance services through UAV-borne Video-SAR: A taxonomy of intelligent data processing for IoMT-enabled radar sensor networks

Khosravi

Samadi

2022

Tsinghua Sci. Technol.

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This study investigates the different aspects of multimedia computing in Video Synthetic Aperture Radar (Video-SAR) as a new mode of radar imaging for real-time remote sensing and surveillance. This research also considers new suggestions in the systematic design, research taxonomy, and future trends of radar data processing.Despite the conventional modes of SAR imaging, Video-SAR can generate video sequences to obtain online monitoring and green surveillance throughout the day and night (regardless of light sources) in all weathers. First, an introduction to Video-SAR is presented. Then, some specific properties of this imaging mode are reviewed.Particularly, this research covers one of the most important aspects of the Video-SAR systems, namely, the systematic design requirements, and also some new types of visual distortions which are different from the distortions, artifacts and noises observed in the conventional imaging radar. In addition, some topics on the general features and high-performance computing of Video-SAR towards radar communications through Unmanned Aerial Vehicle (UAV) platforms, Internet of Multimedia Things (IoMT), Video-SAR data processing issues, and real-world applications are investigated.

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Reliable Data Aggregation in Internet of ViSAR Vehicles Using Chained Dual-Phase Adaptive Interpolation and Data Embedding

Cited by 41 publications

References 26 publications

Generative Adversarial Network for Image Raindrop Removal of Transmission Line Based on Unmanned Aerial Vehicle Inspection

Generative Adversarial Network for Image Raindrop Removal of Transmission Line Based on Unmanned Aerial Vehicle Inspection

A novel triple recovery information embedding approach for self-embedded digital image watermarking

Mobile multimedia computing in cyber-physical surveillance services through UAV-borne Video-SAR: A taxonomy of intelligent data processing for IoMT-enabled radar sensor networks

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