W-VDSR: wavelet-based secure image transmission using machine learning VDSR neural network

Khandelwal, Jyoti; Sharma, Vijay K

doi:10.1007/s11042-023-15166-7

Cited by 3 publications

(5 citation statements)

References 37 publications

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“…Most AI research emphasizes the importance of creating dedicated ANNs for specific purposes, with the majority of deep learning focusing on the classification and quality improvement of general images [ 35 , 36 , 37 , 38 ]. Although the VDSR used in this study shows generalized performance, its image quality improvement metrics tend to be somewhat lower than the PSNR improvement figures for general images [ 32 , 39 ]. In addition, the limited dataset used in the experiments may have led to overfitting.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Super-Resolution Technique Based on Magnetic Resonance Imaging for Application of Image-Guided Diagnosis and Surgery of Trigeminal Neuralgia

Hwang,

Park,

Kang

et al. 2024

Life

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This study aimed to implement a deep learning-based super-resolution (SR) technique that can assist in the diagnosis and surgery of trigeminal neuralgia (TN) using magnetic resonance imaging (MRI). Experimental methods applied SR to MRI data examined using five techniques, including T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), contrast-enhancement T1WI (CE-T1WI), T2WI turbo spin–echo series volume isotropic turbo spin–echo acquisition (VISTA), and proton density (PD), in patients diagnosed with TN. The image quality was evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). High-quality reconstructed MRI images were assessed using the Leksell coordinate system in gamma knife radiosurgery (GKRS). The results showed that the PSNR and SSIM values achieved by SR were higher than those obtained by image postprocessing techniques, and the coordinates of the images reconstructed in the gamma plan showed no differences from those of the original images. Consequently, SR demonstrated remarkable effects in improving the image quality without discrepancies in the coordinate system, confirming its potential as a useful tool for the diagnosis and surgery of TN.

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Section: Discussionmentioning

confidence: 99%

Deep Learning Super-Resolution Technique Based on Magnetic Resonance Imaging for Application of Image-Guided Diagnosis and Surgery of Trigeminal Neuralgia

Hwang,

Park,

Kang

et al. 2024

Life

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“…Also, the concept of image security is not discussed here. (9) Here, it is about storing data in a block of image and then securing them.…”

Section: Continued On Next Pagementioning

confidence: 99%

“…Quantum Steganography is discussed in (8) , where the topic deviates more to the path of physics and the various other methods that can be used to embed data into an image. The paper titled " W-VDSR: wavelet-based secure image transmission using machine learning VDSR neural network" referenced in (9) talks about storing data in a block of image and then securing them. Both of the paper (8) and (9) does not talk about securing the image in return.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Encoding Schemes in Image Steganography Combined with Scrambling for Safeguarding Legal Asset Documents

Yamini,

Priya

2024

IJST

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Objective: The main objective of the study is to provide security to legal asset documents by including the details of ownership of the asset. Methods: This paper aims to improve security of legal asset documents using hybrid encoding schemes in Image Steganography combined with Image Scrambling. The proposed work uses images were gathered from various online sources grouped into a dataset of 40 images along with their 40 text files respectively. The techniques that were applied in the research are pre-processing for the noise removal, Steganography to embed data into image and Scrambling to introduce blur to the image. Finally, the objective metrics like Mean-Square Error (MSE), Peak Signal - Noise Ratio (PSNR) and Signal to Noise Ratio (SNR) values were calculated from them and compared with the acceptable values of the respective metrics. Findings: In the existing study, Steganography and Scrambling has been used separately for data and image security but in the proposed technique, Steganography has been applied with the land ownership data being hidden in the document image and the image being scrambled using different masks. The objective metrics gave the results such as the MSE value ranged between 0.0 and 10.0; the SNR value ranged between 14 dB to 80 dB; the PSNR value lies between 27 dB to 100 dB. The outcome of these values was mostly within the acceptable value range for all the measures here. Novelty: The proposed output dataset contains land asset documents that have details of ownership embedded in itself. Also, the hybrid encoding schemes of both the steganography and scrambling used here gives us data and image security together as proved in the outcome. Keywords: Image Steganography, Scrambling, Text Hiding, Legal Documents, Data Security

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“…*Image coding algorithms have an important role in multimedia image transmission scenarios. Among them, wavelet-based image coding algorithms (Thomas et al, 2023;Khandelwal and Sharma, 2023;Bovik, 2005;Garg and Kumar, 2022;Vetterli and Kovacevic, 1995) are the most popular. To achieve cost-effective transmission (Danyali and Mertins, 2004), the coding algorithms are focused on the region of interest area (ROI) (Salomon, 2004;Manpreet and Wasson, 2015) based concepts.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of modified wavelet difference reduction methods in image compression and transmission

Bindulal

2023

Int. j. adv. appl. sci.

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The wavelet difference reduction (WDR) method, a variant of run-length coding, finds its significance in data transmission applications. Over time, numerous enhanced iterations of WDR methods have emerged. Notably, the Adaptive Scalable WDR method exhibits superior coding gains, as evidenced by the peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM), when compared to its predecessors. This paper conducts an exhaustive examination, encompassing both coding performance and time complexity, of various WDR methods vis-à-vis the conventional image compression algorithm SPIHT. Furthermore, it delves into the performance assessment of diverse coding techniques in the realm of encoding arbitrary-shaped objects. The analysis underscores that modified WDR variants demonstrate remarkable prowess in compression, rendering them invaluable for rapid transmission in bandwidth-constrained networks. To substantiate these findings, coding results (measured in terms of PSNR) are derived from the application of these algorithms to standard test images, MRI images, and video still images. The results reveal coding gains ranging from 0.5 dB to 1 dB for regular resolution images and a substantial 2 dB to 12 dB for scalable resolution scenarios, in comparison to traditional coding approaches. Consequently, this analysis underscores the convenience and superiority of modified WDR methods, not only for still images but also for encoding and transmitting arbitrary-shaped objects.

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W-VDSR: wavelet-based secure image transmission using machine learning VDSR neural network

Cited by 3 publications

References 37 publications

Deep Learning Super-Resolution Technique Based on Magnetic Resonance Imaging for Application of Image-Guided Diagnosis and Surgery of Trigeminal Neuralgia

Deep Learning Super-Resolution Technique Based on Magnetic Resonance Imaging for Application of Image-Guided Diagnosis and Surgery of Trigeminal Neuralgia

Hybrid Encoding Schemes in Image Steganography Combined with Scrambling for Safeguarding Legal Asset Documents

Performance analysis of modified wavelet difference reduction methods in image compression and transmission

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