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Aiming at the problem of high altitude turbulence environment to the time-delay stable image acquisition of UAV, an anti-shaking algorithm for aerial video was proposed for hovering shooting and moving shooting. Firstly from the time-delay photography video captured the UAV camera, some video frames were extracted globally to compare their histogram distributions. This comparison could identify whether the video contained active camera motion or not, and help categorize the video accordingly. For videos with active camera motion, FAST corner detection and optical flow methods were used to extract and match feature points. The RANSAC algorithm could remove all mismatched feature points, and estimate the camera's motion trajectory. The resulting motion estimation parameters were then smoothed using Gaussian filtering, producing a stable camera motion trajectory. For videos without active camera motion, the first frame was divided into grids and feature points were extracted based on Harris matrix. Optical flow tracking was carried out on these feature points in subsequent frames. Reverse optical flow and Harris matrix calculation were used to extract and match feature points, to increase the constraint of feature points. Finally, the retained feature points were used to estimate the stable transformation from subsequent frames to the first frame. Experimental results showed that the video classification module could correctly distinguish between the two types of videos. The algorithm was used to classify the video scene and stabilize the picture. Compared to other methods, this algorithm could improve the average peak signal-to-noise ratio of stabilized video images the most. For videos without active camera motion, the image could be absolutely stable, and the average peak signal-to-noise ratio of the image was increased by more than 39%, while the other two methods only by 10% to 12%. [ABSTRACT FROM AUTHOR]

The crazy, unconscious use of the Internet, and the increase in cybercrime and hacking, which resulted in the loss of a large number of sensitive data, the risk of piracy, etc. were the motivation for protecting rights, securing and hiding essential information such as images, audio, etc. Watermarking was a popular security, copyright and privacy approach and one of the problems in modern quantum digital computing that produced more powerful quantum protection techniques than the classical one that used classical techniques such as digital wavelet transformation and Hungarian algorithms. Recent advances in quantum image representation and processing help to manipulate data hidden. This paper proposes a new watermark technique in which a color watermark image is hidden within another color-carrying image after transformation into the quantum domain using the recently completed quantum color image representation technique. Finally, a comparison is made between the proposed method and the most recent watermarking techniques is establish in terms of PSNR (peak signal-to-noise ratio), which shows the high precision of the proposed technique and improves the imperceptibility compared to the related schemes. [ABSTRACT FROM AUTHOR]

Noise is an unwanted element that has a negative impact on digital image quality. Salt-and-pepper noise is a type of noise that can appear at any point during the acquisition or transmission of images. It is essential to utilize proper restoration procedures to lessen the noise. This paper proposes a hardware-efficient VLSI architecture for the feedback decision-based trimmed mean filter that eliminates high-density salt-and-pepper noise in the images. The noisy pixels are identified and corrected by considering the neighbouring pixels in a 3 × 3 window corresponding to this noisy centre pixel. Either the mean of the horizontal and vertical noisy pixels or the mean of noise-free pixels in the window is computed. This mean value is fed back and the noisy centre pixel is updated immediately, such that this updated pixel value is used henceforth for correcting the remaining corrupted pixels. It is observed that this procedure helps in removing the noisy pixels effectively even if the noise density is high. Additionally, the designed VLSI architecture is efficient, since the algorithm does not require a sorting process and the computing resources required are less when compared to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In the modern world, digital image processing requires increasing the speed of the processing methods and algorithms used. One way to improve performance is to transform spatial filters into filters with a recursively separable form of implementation. The recursion property implies the use of previous output values of a function to form the current sample. The property of separability is understood as division into processing by column and row of a matrix of digital image values. The transformation of spatial filters consists of changing the aperture of the masks into a non-orthogonal (non-equilateral) form, which reduces the number of computational operations and speeds up the processing process, while maintaining its efficiency. The paper presents a description of the non-equilateral apertures of the previously developed Laplacian digital filters "truncated pyramid" and "double pyramid." For non-equilateral apertures, results were obtained for the first time on their use for television measuring systems. From which it can be seen that a "truncated pyramid" Laplacian filter with non-equilateral processing apertures is recommended for use in TIS, since it increases the efficiency of measuring the range to objects of interest while reducing processing time. Based on the results of processing with modified filters, sets of processed images were obtained for each of the 10 original images. For each set of processed images, measurements of the peak signal-to-noise ratio, standard deviation and selection of the optimal central coefficient of the filter mask were carried out, for subsequent assessment of the effectiveness of processing with modified filters. The assessment of the influence of recursively separable Laplacian filters "truncated pyramid" and "double pyramid" with non-equilateral aperture masks on the accuracy of television measurement systems consisted of considering their influence on measuring the distance (from the camera) to the object of interest in the image, when - control of processing time. Based on the evaluation results, we can conclude that by using pre-processing of images with modified digital filters, the accuracy of measuring the distance from the camera to the measurement object is improved, while reducing processing time. [ABSTRACT FROM AUTHOR]

In recent years, digital imaging has played an essential role in medical diagnostics, security and surveillance, robotics engineering, and many other fields. However, noise can occur due to environmental factors or during data transmission, which degrades the image quality and causes information loss. AWGN(additive white gaussian noise) is a typical noise in digital images, and various filters have been studied to remove it. However, the existing methods show somewhat inadequate denoising performance by blurring the image while removing noise. Therefore, in this paper, we propose an AWGN denoising algorithm using a Gaussian weighted filter based on GMM(gaussian mixture model) clustering. The proposed algorithm simultaneously applies a mask for the probability of each pixel belonging to a cluster and a mask that follows a Gaussian distribution in space to improve the denoising performance while preserving the details in the high-frequency region. [ABSTRACT FROM AUTHOR]

Photoacoustic (PA) imaging is hybrid imaging modality with good optical contrast and spatial resolution. Portable, cost‐effective, smaller footprint light emitting diodes (LEDs) are rapidly becoming important PA optical sources. However, the key challenge faced by the LED‐based systems is the low light fluence that is generally compensated by high frame averaging, consequently reducing acquisition frame‐rate. In this study, we present a simple deep learning U‐Net framework that enhances the signal‐to‐noise ratio (SNR) and contrast of PA image obtained by averaging low number of frames. The SNR increased by approximately four‐fold for both in‐class in vitro phantoms (4.39 ± 2.55) and out‐of‐class in vivo models (4.27 ± 0.87). We also demonstrate the noise invariancy of the network and discuss the downsides (blurry outcome and failure to reduce the salt & pepper noise). Overall, the developed U‐Net framework can provide a real‐time image enhancement platform for clinically translatable low‐cost and low‐energy light source‐based PA imaging systems. [ABSTRACT FROM AUTHOR]

Haze and fog can significantly reduce the visibility of distant objects by scattering light and creating blurry, washed-out images lacking in detail. Image dehazing is a technique used in computer vision to enhance the clarity of such obscured images. This study examines the effectiveness of various dehazing methods using real hazy images and synthetic images with artificially created haze. Performance metrics such as PSNR, SSIM, MSE, CII, and computation time are used to evaluate the proposed method. The evaluation is carried out on datasets like RESIDE, GMAN, O-Haze, I-Haze, NH-Haze, and Dense haze to compare the proposed method with existing models. The proposed method attains 27.46%, 20.63% and 21.09% higher PSNR and 12.36%, 23.95% and 36.12% lower Natural Image Quality Evaluator for RESIDE dataset when analyzed to the existing models, such as strategic method towards contrast enhancement by 2D histogram equalization under TV decomposition (CE-TDHE-TVD), multiple level framework basis contrast enhancement for uniform with non-uniform back ground imageries utilizing appropriate histogram equalization (CE-VHE), and Contrast enhancement with brightness preservation of low light pictures with the help of combined CLAHE and BPDHE histogram equalization (CE-CLAHE-BPDHE) respectively. [ABSTRACT FROM AUTHOR]

The focus of this paper is impulse noise reduction in digital color images. The most popular noise reduction schemes are the vector median filter and its many variants that operate by minimizing the aggregate distance from one pixel to every other pixel in a chosen window. This minimizing operation determines the most confirmative pixel based on its similarity to the chosen window and replaces the central pixel of the window with the determined one. The peer group filters, unlike the vector median filters, determine a set of pixels that are most confirmative to the window and then perform filtering over the determined set. Using a set of pixels in the filtering process rather than one pixel is more helpful as it takes into account the full information of all the pixels that seemingly contribute to the signal. Hence, the peer group filters are found to be more robust to noise. However, the peer group for each pixel is computed deterministically using thresholding schemes. A wrong choice of the threshold will easily impair the filtering performance. In this paper, we propose a peer group filtering approach using principles of Bayesian probability theory and clustering. Here, we present a method to compute the probability that a pixel value is clean (not corrupted by impulse noise) and then apply clustering on the probability measure to determine the peer group. The key benefit of this proposal is that the need for thresholding in peer group filtering is completely avoided. Simulation results show that the proposed method performs better than the conventional vector median and peer group filtering methods in terms of noise reduction and structural similarity, thus validating the proposed approach. [ABSTRACT FROM AUTHOR]

The Digital image plays an important role in today’s digital world. Storing and transmitting digital images efficiently is a challenging job. There are lots of techniques for reducing the size of digital pictures. This paper adapts the following method. The digital technique is separated into high and low resolutions. The low intensity and high intensity pixels single-handedly is dense and decompressed using three diverse algorithms to hit upon out the occurrence of low down intensity pixels in the picture. Totally six algorithms are experienced by means of benchmark images and the most excellent scheme is selected for concluding compression. A Comparison is made between the results obtained using these techniques and those obtained using JPEG 2000. [ABSTRACT FROM AUTHOR]

AbstractWe propose a new matrix factor model, named RaDFaM, which is strictly derived from the general rank decomposition and assumes a high-dimensional vector factor model structure for each basis vector. RaDFaM contributes a novel class of low-rank latent structures that trade off between signal intensity and dimension reduction from a tensor subspace perspective. Based on the intrinsic separable covariance structure of RaDFaM, for a collection of matrix-valued observations, we derive a new class of PCA variants for estimating loading matrices, and sequentially the latent factor matrices. The peak signal-to-noise ratio of RaDFaM is proved to be superior in the category of PCA-type estimators. We also establish an asymptotic theory including the consistency, convergence rates, and asymptotic distributions for components in the signal part. Numerically, we demonstrate the performance of RaDFaM in applications such as matrix reconstruction, supervised learning, and clustering, on uncorrelated and correlated data, respectively. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

Cryptography is required while interacting through an insecure media, such as internet. Unauthorised individuals have the ability to view and alter data as it is being transferred from one location to other. Cryptography is important in the realm of security of data. Cryptographic methods are being utilized to protect information against attackers in various areas. Symmetric key encryption employs a single key for encryption and decryption, and is a famously used cryptosystem. In this manuscript, improved elliptical curve cryptography and chaotic mapping with fruitfly optimization algorithm is proposed for Secure Data Transmission (IECC-CMFOA-SDT). Here, cryptography and steganography are consolidated to enhance the data security. Initially, input is the plain text from the sender and compression is done to decrease the size of input data. Then data obfuscation is carried out over the compressed data file with the aid of binary conversion, 2's complement, decimal conversion, and ASCII conversion. By utilizing the improved elliptical curve cryptography (IECC) algorithm, obfuscated data is encrypted. Encrypted data is embedded with the support of Chaotic mapping based fruitfly optimization algorithm (CMFOA), which conceals the data by jumbling the pixels in the image. Hence the secret data is diversified. CMFOA picks the superior values of pixels to root the encrypted data. The receiver acquires the stego image with embedded concealed data, which chooses the optimum pixels points to extract the concealed data from the image. Original data is acquired by decrypting, deobfuscating and decompressing the extracted data. The proposed approach is executed in CloudSim Plus. The performance metrics, like obfuscation time, deobfuscation time, encryption time, decryption time, MSE and PSNR are analysed. Performance of IECC-CMFOA-SDT model gives 39.01%, 28.34%, and 37.45% lower encryption time and 17.12%, 24.12% and 32.07 higher PSNR compared with the existing methods, like optimal users based secure data transmission with lightweight block ciphers (CRT-HTLBO-SDT), Secure data transmission employing a hybrid of a bit mask oriented genetic algorithm, encryption and steganography (BMOGA-DWT-SDT) and cryptanalysis of an encryption method based on a compound coupled logistic map and an anti-codifying technique for secure data transmission (CCLM-ACT-SDT). [ABSTRACT FROM AUTHOR]

Traditional preprocessing methods have problems of low stability and poor universality. To solve these problems, this article conducted effective research on parameter tuning of image preprocessing methods based on the Ant Colony Particle Swarm Optimization (ACPSO) algorithm. This article used peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) as fitness functions. The particle dimension is defined as 2, which represents the size of the median filter and the size of the histogram equalization window. After the iteration, the results of ant colony algorithm and particle swarm optimization (PSO) algorithm were compared, and the parameter with the highest fitness function value was selected as the final preprocessing parameter. The image was preprocessed using the determined optimal parameters. The results showed that the average PSNR and SSIM values of the ACPSO preprocessed images were 5.58 and 0.08 higher than those of traditional preprocessing methods, and the subjective visual evaluation score was also higher. The Otsu's binarization method was used for segmentation. The method of feature extraction using Histogram of Orientated Gradients (HOG) and Local Binary Patterns (LBP), as well as the recognition model using Convolutional Neural Networks (CNN) and Support Vector Machines (SVM), was more stable than traditional preprocessing methods. On the other two datasets, the recognition results of ACPSO preprocessed images performed better and showed better universality compared to traditional preprocessing methods. ACPSO algorithm, as a hybrid swarm intelligence algorithm, can effectively play an effective role in optical pattern recognition image preprocessing, solving the problems of low stability and poor universality of traditional preprocessing methods. [ABSTRACT FROM AUTHOR]

As the demand for information related to visual data has increased in modern society, so has the technology to process it. These advances have revolutionized many fields and are being applied in our daily lives and various industries. However, in processing visual data, Salt and Pepper noise is generated, degrading the video quality. Various types of filters such as CWMF, EEPA, and IAWMF have been studied to remove this noise, but the existing methods show insufficient performance in extremely high noise-density environments. Therefore, in this paper, a RANSAC switching filter for Salt and Pepper noise removal is proposed, and the noise is removed by switching the filter using the filter with RANSAC algorithm, the average filter, and the weighted Median filter according to the conditions. Simulation results show that the proposed algorithm performs effectively in extremely high noise-density environments and has good visual results. [ABSTRACT FROM AUTHOR]

Digital content, such as images, texts, and audio, are now easily accessible on the Internet in a digitalized manner that enables efficient storage and transmission. As information technology evolves concurrently, there has been more cases of cybercrime including data theft and data fabrication by other parties. A wide range of strategies and methods have been used to address these issues using steganography. Steganography is a technique used to prevent unauthorized access of sensitive data by hiding secret information inside digital media containers. This paper aims to investigate the challenges of safely embedding messages into medical images using two different steganographic techniques: Discrete Cosine Transform (DCT) and Least Significant Bit (LSB). An increasingly popular method for hiding sensitive data in the LSBs of pixel values, without causing any discernible deformation is LSB-based steganography. The process of Discrete Cosine Transform alters the structure of the image and embeds the data to enhance its robustness. The DCT and LSB steganography approaches were compared in this work based on the performance parameters. The performance of LSB and DCT methods can be measured by analyzing their SSIM, PSNR, and MSE values. After evaluating different techniques of data hiding in DICOM images, it was found that LSB steganography produces an SSIM value higher than DCT and shows better performance in terms of PSNR values. By observing the results of embedding text and PDF files in DICOM images, it was concluded that LSB steganography provides superior image quality compared to DCT steganography with a data capacity of 2.03 bpp.

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Medical Imaging is an essential practice in radiology to create high-standard images of the human brain. In medical imaging, denoising techniques are essential during image processing for a meaningful view of the anatomical structure of the images. In order to overcome the denoising issues, various filtering techniques and smoothening algorithms have come forth to get an accurate image for better diagnosis while preserving the original image quality. This work utilizes three computational methods for filtering noise that could distort the factual information in MRI images. The input used as the data throughout this study are MR images in grayscale contaminated with Salt and pepper noise, the most common noise in MRI images. To de-noise, a comparative analysis of three specific filters, namely the Non-Local Means filter, Median filter, and Adaptive Median filter, is conducted to do a study that gives the best results among them at different noise densities. Peak Signal-To-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are utilized as the main components to examine the behavior of the suggested filters in this study. The results show that at every value of noise density, i.e., 0.1, 0.3, 0.6, the adaptive median filter gives the highest average PSNR of 42.04, 34.36, and 28.10 and average SSIM of 0.97, 0.95, and 0.91, respectively. Hence, it indicates that the adaptive median filter outperforms the other two filters regarding PSNR and SSIM. [ABSTRACT FROM AUTHOR]

In today's era of widespread web technology and cloud computing, ensuring data security has become a crucial concern across various industries. Instances of data breaches and vulnerabilities in cloud storage have emphasized the need for robust data protection and communication protocols, particularly in sectors like social media, military, and research. This research proposes a Multi-Level Steganography (MLS) algorithm that employs two encryption algorithms, AES and Blow-Fish, to secure the cover image and embed encryption keys as key images within the stego image. The proposed MLS algorithm incorporates a robust pixel randomization function to enhance the security of the encrypted data. Experimental results demonstrate that the proposed algorithm effectively protects data with high Peak Signal-to-Noise Ratio (PSNR) and low Mean Square Error (MSE) values, ensuring superior image quality, reliable encryption, and decryption of secret messages. The utilization of hybrid encryption with AES and BlowFish algorithms further strengthens the algorithm's security by augmenting the complexity of the encryption process. [ABSTRACT FROM AUTHOR]

This paper constructs a Chinese painting color teaching platform based on intelligent image color processing technology. Firstly, the region segmentation method is used to reasonably segment the pixel points according to the similar values of color and texture parameters of the image. Then, the image’s primary color grayscale parameters are fuzzy, detected, equalized, and fused by color difference values. Finally, the gradient optimization algorithm is combined to identify and control the image color parameters. The results show that the highest value of peak signal-to-noise ratio can reach 73.4db for the images processed by the method of this paper, and the mean value of STRESS is kept between 0-10%. The intelligent image color processing technique has been proven to enhance the aesthetics and innovation of Chinese painting colors in students.

This study investigated the effects of absorption attenuation on parametric ultrasound (US) for application to low-frequency (LF) US measurement and imaging. The effects of an absorption layer on the sound field and pulsed wave of parametric US at several hundred kilohertz in water were experimentally evaluated via a comparison with LF US directly radiated from a transmitter. The results indicate that the variations of the shape and amplitude of the parametric US field and waveform due to the insertion of an absorber were more significant than those for LF US at the same frequency. However, these effects can be mitigated when an absorber is inserted at a far distance from a transmitter. These results suggest that for the application of parametric US in measurement and imaging, a low-absorption medium must be inserted between a transmitter and absorbing material to be evaluated in order to grow secondary waves sufficiently. [ABSTRACT FROM AUTHOR]

The need for deep convolutional neural network is increasing for medical image classification because it provides good performance. This work elucidates the significance of convolutional neural network in making effective detection of clinical diseases by categorizing the clinical images in an organized manner. Clinical diseases are difficult to predict and interpret. To predict diseases from medical images, the Stochastic Multinomial Logarithmic (SML) based image classification method is proposed. To effectively eliminate noise from images, edge-boosting locally adapted space-variant filters are first applied to the texture and medical MRI, and CT images. The SML approach is used to improve feature classification and disease prediction. Accuracy, Peak Signal-to-Noise Ratio (PSNR), precision, recall and specificity performances of the proposed approach are compared with surviving methods. The proposed method produces enhanced performance compared to the existing ones with improved accuracies of 95.8% and 96.2 % respectively, for Brodatz texture and brain MRI, CT images. [ABSTRACT FROM AUTHOR]

In the standardization of panoramic radiography quality, the education and training of beginners on panoramic radiographic imaging are important. We evaluated the relationship between positioning error factors and multiple image analysis results for reproducible panoramic radiography. Using a panoramic radiography system and a dental phantom, reference images were acquired on the Frankfurt plane along the horizontal direction, midsagittal plane along the left–right direction, and for the canine on the forward–backward plane. Images with positioning errors were acquired with 1–5 mm shifts along the forward– backward direction and 2–10° rotations along the horizontal (chin tipped high/low) and vertical (left–right side tilt) directions on the Frankfurt plane. The cross-correlation coefficient and angle difference of the occlusion congruent plane profile between the reference and positioning error images, peak signal-to-noise ratio (PSNR), and deformation vector value by deformable image registration were compared and evaluated. The cross-correlation coefficients of the occlusal plane profiles showed the greatest change in the chin tipped high images and became negatively correlated from 6° image rotation (r = −0.29). The angle difference tended to shift substantially with increasing positioning error, with an angle difference of 8.9° for the 10° chin tipped low image. The PSNR was above 30 dB only for images with a 1-mm backward shift. The positioning error owing to the vertical rotation was the largest for the deformation vector value. Multiple image analyses allow to determine factors contributing to positioning errors in panoramic radiography and may enable error correction. This study based on phantom imaging can support the education of beginners regarding panoramic radiography. [ABSTRACT FROM AUTHOR]

Medical images are one of the most critical and sensitive types of data in information systems. For the secure storage and transfer of medical images, confidentiality is the most important aspect. This paper presents efficient embedding technique to enhance medical image security. The Gaussian filters are used as preprocessing to remove high frequency components and then applied to cumulative distribution function (CDF) 5/3 wavelet to obtain LL band features. Similarly, the LL band features of cover image are obtained. The alpha bending technique combines both the LL band features of cover and secret image to form first level of encryption and now with other high frequency bands of LH, HL, and HH applied to Inverse CDF 5/3 obtains an encrypted image which is then transferred along with key obtained through other bands of LH, HL, and HH. The key generated acts as additional level of security and similarly, at the receiver the opposite operations are performed to obtain the original secrete image. The performance is measured in terms of Peak signal-to-noise ratio (PSNR) and is compared with existing techniques to validate the results. Further, the entire architecture is synthesized on spartan 6 field-programmable gate array (FPGA) board to compare the hardware utilizations. [ABSTRACT FROM AUTHOR]

With the advent of several new means of communication, safeguarding the confidentiality of messages has become more crucial. Financial institutions, virtual currencies, and government organizations are all examples of high-risk contexts where information exchanges need particular care. The importance of data security in preventing unauthorized access to data is emphasized. Several cryptographic methods for protecting the secrecy and integrity of data were compared. In this research, the proposed work includes a new Turbo Code-based encryption algorithm. The Turbo encoder's puncturing process is controlled by a secret key, and a typical random sequence is generated to encrypt the data and fix any mistakes. Key generation utilizing pre-existing data eliminates the requirement for sending keys over a secure channel. Using recurrence relations and the Lower–Upper (LU) decomposition method, the presented study suggests a novel approach to message encryption and decryption. The resulting encrypted grayscale image has a very high level of security, with an entropy of 7.999, a variation from perfection of 0.0245, and a correlation of 0.0092 along the diagonal, 0.0009 along the horizontal, and −0.0015 along the vertical. Directly decrypted pictures have a Peak Signal-to-Noise Ratio (PSNR) of 56.22 dB, but the suggested approach only manages an embedding capacity of 0.5 bpp (bits per pixel). This may be achieved by decreasing the size of the location map by only 0.02 bpp. [ABSTRACT FROM AUTHOR]

This study proposes an efficient dual image-based reversible fragile watermarking scheme (DI-RFWS) that can accurately detect and locate the tampering regions from an image. The proposed scheme embeds two secret bits in each host image (HI) pixel using a pixel readjustment strategy to obtain dual watermarked images (WIs). The pixel readjustment strategy performs a maximum modification of ± 1 to the non-boundary pixels of an image based on the watermark information. The results of the study suggest that in addition to reversibility, the proposed scheme offers triple objective of high capacity, better perceptual transparency, and robustness. Experimental results also show that the proposed scheme achieves a superior peak signal-to-noise ratio (PSNR) of above 52 dB for both the WIs. Further, the proposed scheme can efficiently detect and locate the tampering regions from an image with a high true positive rate, low false positive and negative rate for various tampering rates. Additionally, the proposed scheme shows superior resistance against various intentional and unintentional attacks. [ABSTRACT FROM AUTHOR]

The past decades witness a tremendous exchange in data and information in almost all domains. This leads to the need for applying data security to ensure information safety during communication. One of the main used technique is the steganography. Steganography is a technique that involves hiding information within objects, such as images, audio, video, or 3D objects, without leaving any noticeable alterations. We propose a 3D steganographic technique that increases security using Gray-code sequence and a Least Significant Bit technique. We construct the 64-binary representation for the 3D cover-object. A Gray-code sequence defined the 3D object vertices that will be utilized for concealing the information. The binary representation of the information is to be concealed in the least significant bits of either the x- or the y-components of the defined vertices. The least significant bits, are the last two bits in the binary representation of the vertex components. Exploitation the binary representation, gives us the capability of increasing the data security, and the data capacity. The proposed steganographic technique has been evaluated through multiple evaluation metrics to assess its performance; including Mean Square Error Ratio, Peak Signal-to-Noise Ratio, Histogram, and Normalized Correlation Experimental results, indicate that the security level of the proposed technique is superior comparing to several existed techniques. Moreover, when testing against some common attacks such as the noise, the median, the Gaussian and the Laplacian filters; the results show the efficiency and the robustness of the proposed technique.

With the tremendous growth of searchable visual media, the content-based medical image retrieval and computer-aided diagnosis systems have become popular in recent years to improve knowledge and provide facilities for radiologists. Medical images transferred throughout public networks demand a mechanism that guarantees image privacy, ownership and source of origin reliability, and image integrity verification. For this reason, secure image retrieval and diagnosis scheme have been given considerable interest due to users’ security concerns. This work proposed a secure framework based on a two-dimensional (2D) chaotic map with Diffie–Hellman key exchange protocols to ensure patient information privacy and security. Consequently, from a security and protection perspective, the objective is to provide a privacy procedure for medical image retrieval systems through image encryption technique combined with a secure key exchange procedure to minimize the possibility of secret key interception by an unauthorized person. Simulation results and security analysis show that the suggested technique could protect images with minimal time complexity and a high level of security while also resisting numerous attacks.

A novel method for filtering ultrasonic and magnetic resonance images contaminated by multiplicative (speckle) noise is justified and implemented. The method consists of several stages: segmentation of image areas, grouping of similar structures in 3D, homomorphic transformation, a 3D filtering approach based on a sparse representation in the contourlet transform (CLT) space with posterior filtering according to MI weights of similar 2D structures, and the final inverse homomorphic transformation. A physical interpretation of the filtering procedure in the case of speckle noise is given, and a structural scheme for noise suppression is developed. Simulation based on the new filtering procedure has confirmed its superiority in terms of generally accepted criteria, such as the structural similarity index measure, peak signal-to-noise ratio, edge preservation index, and the resolution index alpha, as well as in a visual comparison of filtered images. [ABSTRACT FROM AUTHOR]

It is broadly recognized that conserving the essential geometrical features of an image is crucial while denoising it. To accomplish this aim, various denoising techniques have been represented in the literature. The technique based on dual way edge fusion can efficiently solve the problem of denoising. In this paper, an efficient denoising scheme using an innovative method of calculating the image base and details is being proposed. The noisy image is thresholded to remove extra noise by the bitonic filter. Details of the discontinuities is extracted by subtracting the recovered image from the noisy image. Subsequently, details features are subtracted from the noisy image to extract the base information. After that, image features and noise are simultaneously filtered by rolling guidance filter to remove the remaining noise from the features and the significant edge information from the filtered noise. The two images are fused with maximum coefficient value to enhance the information content and visual quality of denoised image. The proposed Dual Way Residue Noise Thresholding (DWEFD) is a combination of various spatial and transform domain commutations performed parallelly. Extensive experimental results and investigations reveal that the proposed methodology is able to recover feature details of an image thereby reducing information loss along with efficient noise removal.