Average PSNR Value Research Articles

High capacity image steganography using LSB with modified binary addition on RGB indicator

Steganography is the practice of hiding information in the code that makes up digital files and preventing unwanted people from knowing the existence of hidden information. High-capacity steganography techniques have been successfully performed using LSB with binary addition method, but the quality can be improved using LSB with modified binary addition. By using binary addition, message insertion can be done with two bits at once in one insertion process. For six examples of images, the message insertion process is carried out using the binary addition method and modified binary addition method with the use of 100% capacity or using all pixel values of the image. Then the SSIM, MSE, and PSNR values are calculated from each test result. This study shows that the modified binary addition method has better quality than the binary addition method. The test results show that the binary addition method produces average SSIM, MSE, and PSNR values respectively 0.971743, 9.711678, and 38.26588. Meanwhile, modified binary addition resulted in the values of 0.993287, 2.112745, and 44.88844. These results indicate that the modified binary addition method produces better quality than binary addition while maintaining high message capacity.

An improved separable reversible patient data hiding algorithm for E-healthcare

In the telemedicine industry, a standout among the most significant issue is the exchange of Electronic Patient Information (EPI) between patient and a doctor that are remotely connected. A minute change to EPI may result in a wrong diagnosis for the patient. To ensure secure and safe communication for telemedicine applications, a dual-image separable block-based reversible data hiding algorithm in encrypted domain for embedding secret message in base5 numeral framework is proposed here. The proposed scheme has not been suffering from underflow and overflow problem so that empowering it to embed and recover information precisely from low-intensity pixels too. This property makes our proposed methodology truly reasonable for its utilization on medical images. To prove the effectiveness of our proposed approach, experiments have been performed on different test images. The average PSNR value is 54.10 dB for an embedding capacity of 327,680 bits for all test images which demonstrates that the method is capable of giving good quality stego images even at high payload also. The experimental study revealed that for all types of test images, the proposed methodology altogether beaten all the compared methodologies in its ability to embed secret message and precisely recover it by maintaining the visual quality of stego images too.

An Abdominal Registration Technology for Integration of Nanomaterial Imaging-Aided Diagnosis and Treatment.

Image registration technology is a key technology used in the process of nanomaterial imaging-aided diagnosis and targeted therapy effect monitoring for abdominal diseases. Recently, the deep-learning based methods have been increasingly used for large-scale medical image registration, because their iteration is much less than those of traditional ones. In this paper, a coarse-to-fine unsupervised learning-based three-dimensional (3D) abdominal CT image registration method is presented. Firstly, an affine transformation was used as an initial step to deal with large deformation between two images. Secondly, an unsupervised total loss function containing similarity, smoothness, and topology preservation measures was proposed to achieve better registration performances during convolutional neural network (CNN) training and testing. The experimental results demonstrated that the proposed method severally obtains the average MSE, PSNR, and SSIM values of 0.0055, 22.7950, and 0.8241, which outperformed some existing traditional and unsupervised learning-based methods. Moreover, our method can register 3D abdominal CT images with shortest time and is expected to become a real-time method for clinical application.

Steganographic Model for encrypted messages based on DNA Encoding

Information has become an inseparable part of human life. Some information that is considered important, such as state or company documents, require more security to ensure its confidentiality. One way of securing information is by hiding the information in certain media using steganography techniques. Steganography is a method of hiding information into other files to make it invisible. One of the most frequently used steganographic methods is Least Significant Bit (LSB).In this study, the LSB method will be modified using DNA Encoding and Chargaff's Rule. Chargaff's Rule or complementary base pairing rule is used to construct a complementary strand. The modification of the LSB method using DNA encoding and Chargaff's Rule is expected to increase the security of the information.The MSE test results show the average value of the LSB method is 0.000236368, while the average value for the DNA Encoding-based Steganography method is 0.000770917. The average PSNR value for the LSB method was 76.82 dB while the DNA Encoding-based Steganography method had an average value of 70.88 dB. The time of inserting and extracting messages using the Steganography method based on DNA Encoding is relatively longer than the LSB method because of its higher algorithmic complexity. The message security of the DNA Encoding-based Steganography method is better because there is encryption in the algorithm compared to the LSB method which does not have encryption.

Secret data modification based image steganography technique using genetic algorithm having a flexible chromosome structure

In all forms of confidential communication, the most significant element is security. Cryptography can be used to secure the information, but it discloses the presence of covert communication. Hence, steganography was invented; steganography is an art of concealed communication. Steganography is performed by inserting the secret information in a cover media to camouflage the presence of covert communication. In image steganography, an image is used as the cover media for secure communication. There are various image steganography techniques invented by many researchers, but a small number of them focus on improving visual quality and increasing payload capacity simultaneously. This paper presents a secret data modification based high capacity image steganography technique using genetic algorithm (GA). This new technique uses the least significant bit (LSB) replacement steganography, to embed the secret data. However, the secret data is rearranged and modified before embedding it in the LSBs of the cover image. The parameters used to rearrange and modify the secret data are controlled by GA. A unique concept called flexible chromosome is introduced which allows GA to interpret the chromosome value in different ways. GA attempts to find the best possible parameter value that yields high visual quality of the stego images. The stego images produced by the proposed technique have an average PSNR value of 46.41 dB and 40.83 dB for 2 bit per pixel (bpp) and 3 bpp data hiding capacity respectively.

A novel chaotic IWT-LSB blind watermarking approach with flexible capacity for secure transmission of authenticated medical images

Nowadays, secure medical data transmission is an essential issue in telemedicine through unsecured channels. Watermarking methods are widely used to provide optimal security for medical image transmission. In this method, a secure and blind watermarking algorithm based on Integer Wavelet Transform (IWT), Least Significant Bit (LSB), and chaotic sequences (ILC) with a high capacity for grayscale medical and non-medical images is ILC. In this scheme, the high-level security is achieved by chaotic sequences that are applied for watermark encryption and determining the location of host blocks and host coefficients in the embedding process. At first, the cover medical image is divided into two distinct parts: the region of interest (ROI) and the region of non-interest (RONI) areas. Due to the high sensitivity of the ROI data in disease diagnosis, no embedding is done in this area. An ROI integrity check data (ICR), produced by some significant features from ROI, to detect tampered blocks inside this area. The main watermark, which is the encryption of ICR along with the patient’s personal and medical information, is embedded in the middle-frequency at the first and second levels of IWT sub-bands. Furthermore, to verify the watermark, integrity check data (ICW) is produced and sent along with other essential data to the receiver. For identifying authentication purposes, the physician’s signature is embedded at the third level (LL3) sub-band of IWT which has the highest energy and robustness. Moreover, the algorithm is flexible for using different sizes of text watermarks and it guarantees a high payload capacity. It is noteworthy the maximum lengths of text watermarks used in the method for 512×512 and 1024×1024 images are 47,784 and 164,620 bits, respectively. Although the size of the embedded watermark increases with the size of the ROI area, the image transparency remains almost constant and also the watermark is extracted with a bit error rate of zero. By doing various analyses, the experimental results represent superior imperceptibility by an average PSNR value of 75 dB. This method provides high imperceptibility and robustness against various attacks compared with state-of-the-art algorithms.

Aplikasi Perbandingan Sistem Perbaikan Citra Digital menggunakan Metode Dekonvolusi Wiener, Lucy Richardson, dan Regularized

In some cases, there is some damage to an image caused by interference during the image capture process. Blurred image damage can be overcome by deconvolution digital image processing. There are various methods to repair the image blur damage, including using the Regularized, Wiener, and Lucy Richardson deconvolution methods. Each blurring image repair method produces a different debluring result of image processing. Image comparison application was built to compare the ability of image restoration results to a Motion Blur image with the algorithms used in deconvolution. Image restoration comparison parameters used include determining the MSE and PSNR values between the test image and the deconvolved image. The results of implementing the comparative application of Motion Blur image improvement to 270 blur simulations consisting of 9 different levels of image blurring, obtained the average PSNR value for Wiener's deconvolution = 59.16dB, Lucy Richardson = 26.92dB and Regularized = 36.94dB.Keywords:Image Restoration; Lucy Richardson; Motion Blur; Regularized; Wiener.

A Coarse-to-Fine Registration on 3D Multi-Phase Abdominal CT Images

Registration is a technical support for the integration of nanomaterial imaging-aided diagnosis and treatment. In this paper, a coarse-to-fine three-dimensional (3D) multi-phase abdominal CT images registration method is proposed. Firstly, a linear model is used to coarsely register the paired multiphase images. Secondly, an intensity-based registration framework is proposed, which contains the data and spatial regularization terms and performs fine registration on the paired images obtained in the coarse registration step. The results illustrate that the proposed method is superior to some existing methods with the average MSE, PSNR, and SSIM values of 0.0082, 21.2695, and 0.8956, respectively. Therefore, the proposed method provides an efficient and robust framework for 3D multi-phase abdominal CT images registration.

Robust image steganography approach based on RIWT-Laplacian pyramid and histogram shifting using deep learning

Nowadays, highly sensitive medical images are vulnerable to data threats and privacy attacks. They must be kept secure while transmitting them across insecure channels precisely for this purpose. The robust image steganography is focused on this work by exploiting Redundant Integer Wavelet Transform (RIWT), Laplacian Pyramid, Arnold scrambling and Histogram shifting algorithm to facilitate secure communication of secret images in the context. Stego images thus generated are subjected to a deep learning approach to assess if it can be classified as a cover or not. If not, the HS parameter is modified to generate stego images in such a way to classify it as a cover image. Thus it is difficult to suspect the existence of a secret image by the Human Visual System (HVS). The efficiency of our method is analyzed by comparing it with related methods present in the literature. Average NCC values between the original secret image and the extracted secret image are 0.8917 which is higher than the schemes in the literature. Average PSNR values of the stego image are 36.375 even when the embedding rate is increased to 4 bits per pixel. The analysis was done on security and robustness also reveals better results. From the experimental analysis, it is proved that the proposed method is superior to the related methods of the literature.

Comparative Analysis of Image Quality Values on Edge Detection Methods

Identification of object boundaries in a digital image is developing rapidly in line with advances in computer technology for image processing. Edge detection becomes important because humans in recognizing the object of an image will pay attention to the edges contained in the image. Edge detection of an image is done because the edge of the object in the image contains very important information, the information obtained can be either size or shape. The edge detection method used in this study is Sobel operator, Prewitt operator, Laplace operator, Laplacian of Gaussian (LoG) operator and Kirsch operator which are compared and analyzed in the five methods. The results of the comparison show that the clear margins are the Sobel, Prewitt and Kirsch operators, with PSNR calculations that produce values ​​above 30 dB. Laplace and LoG operators only have an average PSNR value below 30 dB. Other quality comparisons use the histogram value and the contrast value with the highest value results in the Laplace and LoG operators with an average histogram value of 110 and a contrast value of 24. The lowest histogram and contrast value are owned by the Sobel and Prewitt operators.

Glaucoma detection of retinal images based on boundary segmentation

<p>The rapid growth of technology makes it possible to implement in immediate diagnosis for patients using image processing. By using morphological processing and adaptive thresholding method for segmentation of optic disc and optic cup, various sizes of retinal fundus images captured through fundus camera from online databases can be processed. This paper explains the use of color channel separation method for pre-processing to remove noise for better optic disc and optic cup segmentation. Noise removal will improve image quality and in return help to increase segmentation standard. Then, morphological processing and adaptive thresholding method is used to extract out optic disc and optic cup from fundus image. The proposed method is tested on two publicly available online databases: RIM-ONE and DRIONS-DB. On RIM-ONE database, the average PSNR value acquired is 0.01891 and MSE is 65.62625. Meanwhile, for DRIONS-DB database, the best PSNR is 64.0928 and the MSE is 0.02647. In conclusion, the proposed method can successfully filter out any unwanted noise in the image and are able to help clearer optic disc and optic cup segmentation to be performed.</p>

A new image steganography method with optimum pixel similarity for data hiding in medical images

Steganography is one of the approaches used in data hiding. Image steganography, is a type of steganography that the image is used as a covering object. Data hiding capacity and image quality of the cover object are important factors in image steganography. Because the deterioration of image quality can be noticed by the human vision system, it attracts the attention of attackers. Therefore, the purpose of this study is increasing the amount of data to be hidden and stego image is to ensure high image quality. In the study, a new optimization-based method has been proposed by making use of the similarities of the pixels. In order to test the performance of the proposed method has been used visual quality analysis metrics such as MSE, RMSE, PSNR, SSIM and UQI. As a cover object; different sizes medical images have been used that obtained from the open access Dicom library database. Doctor comments in different capacities have been hidden to the medical images. Experimental results show that the average PSNR value is 66.5374, 59.4420 and 56.3936, respectively, when 1000 characters, 5000 characters and 10,000 characters data is hidden in 512 × 512 images. In addition, the average PSNR value is 60.4308, 53.3529 and 47.4113, respectively, when 1000 characters, 5000 characters and 10,000 characters data is hidden in 256 × 256 images. 10,000 characters of data have not been hidden in 256 × 256 images without using data compression techniques with classical similarity based LSB method. In the proposed method, 10,000 characters of data have been hidden in 256 × 256 size images without using data compression techniques.

Robust IHWT- CRT Image Watermarking using YCbCr Color Space

This research proposes a watermarking method that combines Integer Haar Wavelet Transform (IHWT) and Chinese Remainder Theorem (CRT) algorithms on RGB images that are converted to YCbCr color space. The goal is to get a watermarking method that is robust, imperceptible and secure. Robust because with IHWT done on component Y as a place to insert a watermark done on the frequency domain that is resistant to attack. Whereas CRT is an insertion method in a spatial domain that is relatively more imperceptible to human vision, CRT also more secure because CRT is a cryptographic algorithm. Based on the results of the test and bookkeeping of the imperceptibility quality, the average PSNR value is above 48dB and the average SSIM is above 0.99. Whereas the robustness test proves that by using the YCbCr color space the increase in resistance occurs in Gaussian noise, JPEG compression and JPEG 2000 compression with an average NC value above 0.8.

РОЗРОБКА СТЕГАНОГРАФІЧНОГО МЕТОДУ ДЛЯ ЦИФРОВИХ ЗОБРАЖЕНЬ НА ОСНОВІ ПЕРЕТВОРЕННЯ ФУР'Є

In the modern Internet space in conditions of continuous exchange of information the need to protect it from the possibility of unauthorized use by third parties is increasing. This can be ensured by using steganographic methods that allow organizing a covert communication channel. In the article a new steganographic method for digital images based on the fast Fourier transform developed. As additional information you can use both text messages and images translated into a binary sequence. For embedding of additional information 2×2 blocks are used for which the fast Fourier transform is calculated. Message embedding is done by modifying the absolute difference between two coefficients of block’s Fourier transform. Up to four bits of a binary sequence can be embedded in each block. The results of computational experiments aimed at assessing the effectiveness of the proposed method have shown that high quality of stegos is provided (average PSNR values are 58-60 dB) compared to modern analogues while ensuring high capacity of the covert communication channel (0.75 and 1 bit/pixel). The average values of the extraction accuracy index of additional information NCC are from 0.87 to 0.97 depending on the selected color component and the number of embedded bits. The accuracy of message extraction depends on the characteristics of the container - detection errors occur when there are a large number of blocks with brightness values close to 0 or 255. However, this problem can be eliminated by preliminary analysis of the container. The developed method showed high resistance to attack by the noise "Salt and pepper" (d=0.01) at appreciable distortions of the filled container, as well as the imposition of a Gaussian and a multiplicative noise at imperceptible distortions of stegos.

Imperceptible Steganography Scheme with High Payload Capacity using Genetic Algorithm and Particle Swarm Optimization

Security is the most significant parameter in all type of confidential data transfers. Steganography is used to enhance the security of such confidential communications. Steganography is a method of covert communication in which the existence of secrecy is concealed. In image steganography, achieving high data embedding capacity and simultaneously retaining good visual quality is a very tricky and difficult objective. In this paper, a reversible, secure, extremely imperceptible and high payload capacity steganography technique in the spatial domain is proposed. The proposed method employs evolutionary computation techniques to identify the most optimum locations and arrangements for secret data embedding. The proposed technique uses Particle Swarm Optimization to find the best possible order of data hiding whereas Genetic algorithm is used to identify the best possible arrangements to modify secret data to produce least amount of change in cover-image. The result of the proposed scheme is compared with many steganography techniques and the proposed scheme outperforms the existing schemes in terms of imperceptibility. The proposed technique produces an average PSNR value of 46.40 dB at 2 bit per pixel data embedding rate.

An Accession for Image Denoising by Multiple Filter and Correlative Examination for Random Impulse Noise

Denoising an image is a significant problem in the processing of digital images. Any impulse noise damages the image and the aim of denoising is to remove noise and restore the high-quality image as much as possible. This paper aims to develop a method to discriminate between corrupted and uncorrupted pixels and develop a novel filter to denoise the image. It is also necessary to consider images with different level of noise of various applications to develop an optimal system to remove the noise for further processing. In this paper different filtering techniques such as Median-Filter (MF), Weighted -Median-Filter (WMF),Centre-Weighted Median filter (CWMF), and adaptive centre weighted median filter (ACWMF) are used for denoising, also a novel filter Asymmetric Trimmed Median Filter(ATMF) is designed which outperforms compared to the filters designed earlier. Experimentation is carried by considering the dataset size of 700 noisy images. The average PSNR value of proposed system is 28.12%(DB).

Improved homomorphic filtering using fractional derivatives for enhancement of low contrast and non-uniformly illuminated images

The main objective of the image enhancement is to improve the visual appearance or quality of an image. In this paper, the proposed scheme aims to improve the performance of the homomorphic filtering by employing the fractional derivatives with Discrete Fourier Transform (DFT) and Fractional Fourier Transform (FrFT). FrFT in combination with fractional derivative provides two fractional orders as extra degrees of freedom, thus, providing more design flexibility. This paper uses Grunwald-Letnikov (GL) fractional derivative to enhance the high and mid frequency components non-linearly while preserving the low frequency components. In the proposed approach, modification of homomorphic filtering technique is done on the basis of fractional derivative and FrFT to enhance the low contrast and non-uniformly illuminated images. The effectiveness of the proposed work is evaluated on the basis of various image assessment parameters such as PSNR, information entropy, universal image quality index, etc. on several images of different sizes. The proposed scheme outperforms the existing state-of-the-art techniques by providing better image visual quality and image information in terms of average PSNR and entropy values. The improvement in the average PSNR and information entropy is in the range 0.2635–50.37 dB and 0.02–42% respectively for standard images as well as for images with different contrast and illumination conditions.

Facial image retrieval on semantic features using adaptive mean genetic algorithm

The emergence of larger databases has made image retrieval techniques an essential component and has led to the development of more efficient image retrieval systems. Retrieval can either be content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Adaptive Mean Genetic Algorithm (AMGA). In this study, the average PSNR value obtained after applying the Wiener filter was 45.29. The performance of the AMGA was evaluated based on its precision, F-measure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.

QR code–based ECG signal encryption/decryption algorithm

The paper presents a novel and enhanced bio-signal (ECG) cryptographic technique in which a QR code has been successfully utilized as a data container. The bio-signal encryption process considers ECG samples in the form of integers in order to create corresponding binary pair sequences that are converted as equivalent ciphertext information. Subsequently, the generated ciphertexts are transformed into QR code by using a QR code generator/reader in order to ensure further security. The encryption process generates a binary key for each binary pair that will be used during signal decryption. The imperceptibility of a reconstructed ECG signal is evaluated by analyzing performance metrics in terms of PSNR and PRD values. The average PSNR values are found to be above 42 dB, whereas PRD values observed were less than 3.43 percent. It is further proved that the proposed technique can encrypt a maximum of 226 kb of diagnose data by using merely seven QR codes.

Payload Enhancement on Least Significant Bit Image Steganography Using Edge Area Dilation

This research proposes a method to enhance the payload message by embedding messages on the dilated edge areas by the Least Significant Bit (LSB) method. To add security aspects to messages, messages are not embedded directly on the LSB but encrypted with XOR operations with Most Significant Bit (MSB). The experimental results of the test in this study showed that the dilation process to some extent can increase the payload of 18.65% and the average bpp is 1.42 while maintaining the imperceptibilty quality of stego image with an average PSNR value of about 47 dB, SSIM is 0.9977 and MSE is 1.13.