Spatial Domain Algorithm Research Articles

Effectiveness of the Spatial Domain Techniques in Digital Image Steganography

Digital steganography is a new and extremely demanding method for transmitting information securely over the internet while employing a covert device. Since its inception in the 1990s till the present, digital steganography has a lengthy history. Early steganography focused primarily on imperceptibility, security and embedding capacity. In addition to using statistics as a foundation, convolution neural networks (CNN), generative adversarial networks (GAN), coverless approaches, and machine learning are all used to construct steganographic methods. Robustness is becoming a crucial component of many innovative techniques. Spatial, Transform, and Adaptive domains serve as the understructure of those novel methods. This broadens the range of steganographic technique development and often concentrates the implementation of adaptive techniques. As a result, this study helps to analyze the fundamentals of image steganography, a comparative review on the spatial domain algorithms. As using evaluation tools is strongly tied to the effectiveness of steganography, this study also goes into great detail about its application. The purpose of this research is to determine the best and most effective algorithm among the three competitive spatial domain algorithms, which are Least Significant Bit (LSB), Optimum Pixel Adjustment Procedure (OPAP), and Pixel Value Differencing (PVD) which in regard demonstrated the efficacy of spatial domain algorithms.

Comparing the spatial domain and frequency domain algorithms for upward continuation of marine magnetic anomaly

Space potential field continuation is a method frequently used for the spatial transformation of magnetic anomaly in the sea area, and is the key technology for constructing three-dimensional marine magnetic field model and the application of marine magnetic survey data. In this study, we analyze the principle of upward continuation of potential field from plane to plane, demonstrate the spatial domain algorithm and frequency domain algorithm, and propose the standards for evaluating the accuracy of the two algorithms. Using the theoretical data of the multi-dipole sphere model, we employ the two algorithms to continue the magnetic anomaly of the observation plane at the height 0 m to 100 m, 200 m, and 300 m, for noisy and noise-free conditions. The results show that the continuation error of the two methods is extremely small at the plane center, but large on the edge; the spatial domain algorithm has obviously better accuracy than the frequency domain algorithm, so it should be preferentially selected in high-precision applications, such as magnetic anomaly detection in the sea area.

Robust, Secure and Semi-Blind Watermarking Technique Using Flexible Scaling Factor in Block-Based Wavelet Algorithm

Multimedia security has received much attention recently because of the rapid transmission of elements such as text, images, audio, video, software, animation and games. Security is becoming especially critical for content owners concerned about the illegal usage of their original products. Encryption and watermarking are two methodologies for digital applications. Spatial domain and frequency domain watermarking algorithms give very promising results in embedding binary images into the cover images. This paper proposed a new method of semi-blind watermarking technique. The digital images are divided into 4 × 4 blocks and converted into discrete Wavelet transformations (DWTs). The binary image is embedded into each block using the flexible scaling factor method. Experimental results show that the proposed method has higher peak signal to noise ratio (PSNR) and similarity ratio (SR) values compared to the standard Wavelet transformation and block-based Wavelet algorithms. The results prove that the proposed hybrid algorithm is more effective, robust, secure and resistant than DWT and block-based DWT algorithms.

Design and Embedded Implementation of Secure Image Encryption Scheme Using DWT and 2D-LASM.

In order to further improve the information effectiveness of digital image transmission, an image-encryption algorithm based on 2D-Logistic-adjusted-Sine map (2D-LASM) and Discrete Wavelet Transform (DWT) is proposed. First, a dynamic key with plaintext correlation is generated using Message-Digest Algorithm 5 (MD5), and 2D-LASM chaos is generated based on the key to obtain a chaotic pseudo-random sequence. Secondly, we perform DWT on the plaintext image to map the image from the time domain to the frequency domain and decompose the low-frequency (LF) coefficient and high-frequency (HF) coefficient. Then, the chaotic sequence is used to encrypt the LF coefficient with the structure of “confusion-permutation”. We perform the permutation operation on HF coefficient, and we reconstruct the image of the processed LF coefficient and HF coefficient to obtain the frequency-domain ciphertext image. Finally, the ciphertext is dynamically diffused using the chaotic sequence to obtain the final ciphertext. Theoretical analysis and simulation experiments show that the algorithm has a large key space and can effectively resist various attacks. Compared with the spatial-domain algorithms, this algorithm has great advantages in terms of computational complexity, security performance, and encryption efficiency. At the same time, it provides better concealment of the encrypted image while ensuring the encryption efficiency compared to existing frequency-domain methods. The successful implementation on the embedded device in the optical network environment verifies the experimental feasibility of this algorithm in the new network application.

Frequency Domain Image Reconstruction for Imaging With Multistatic Dynamic Metasurface Antennas

Dynamic metasurface antennas (DMAs) have recently been introduced as a computational imaging (CI) platform that offers significant advantages over conventional imaging systems. Such antennas are able to produce tailored radiation patterns that are later used to encode the scene’s information into a few measurements. However, since the signal is compressed, image reconstruction in the frequency domain using the conventional range migration algorithm (RMA) cannot be directly applied to CI-based apertures synthesized with DMAs. More sophisticated algorithms need to be developed to overcome this limitation, which transfers the complexity of such systems to the software layer. This paper proposes a new multistatic DMA RMA (MD-RMA) technique that achieves decompression of the compressed signals collected from multistatic DMA apertures and processes the decompressed data in the Fourier domain followed by a multistatic-to-monostatic conversion. Simulation results verify that the proposed approach can produce high quality 3D radar images, which alleviates the need to mechanically move the aperture. Moreover, since the image reconstruction is fully conducted in the frequency domain, leveraging the fast Fourier transform (FFT) algorithm, the complexity of the algorithm, and thus the execution time, is significantly reduced in contrast with conventional spatial domain algorithms.

Using gravity gradient component and their combination to interpret the geological structures in the eastern Tianshan Mountains

SUMMARY As early as the Palaeozoic era, the Tianshan Mountains constituted a vast tectonic convergent zone within the Eurasian Continent; by this time, they had already experienced multiple intense crustal movements. The Indian Plate is currently advancing northwards by more than 50 mm yr–1; it affects the Tianshan Mountains even though they are located more than 1000 km away. However, many of the geological issues of the Tianshan Mountains, especially in eastern Tianshan, which remains an active tectonic zone today, are not yet fully understood. There have been few studies into the seismogenic characteristics of this intense seismic zone. Here, the full gravity gradient tensor (GGT) within the eastern Tianshan Mountains was calculated using a stable spatial frequency domain algorithm, based on high-spatial resolution (7.2 arcsec or approximately 200 m) Global Gravity Model plus (GGMplus) surface gravity data. In addition, the features of the geological structures in the eastern Tianshan Mountains were interpreted using different combinations of GGT components. Moreover, the possibilities of using different combinations of GGT components to identify the distributions and strikes of faults was discussed regarding the potential earthquake rupture risks in this study area. The results show that (1) the distributions and strikes of the main fault zones in the study area are highly consistent with the linear geological features within the mountains, as extracted from a combination of different GGT components, including terrain effects. (2) Removing the terrain effects revealed that the different components of GGT (derived from the complete Bouguer gravity anomaly) vary gently, showing few linear geological features that could be extracted. This implies that satellite gravity data are not sufficient to analyse the characteristics of small underground geological structures (spatial scales less than ∼10 km). (3) Comparing the spatial distributions of fault traces depicted at different ages with the linear geological features of the study area revealed that discovering more unknown faults increased the coincidence ratio of linear geological features to fault traces in the area (from 27 to 40 per cent). In conclusion, the findings of this study represent a valuable reference for further revealing the seismogenic characteristics of this area, where there is a lack of detailed surface fault structure measurements due to its inaccessibility. The proposed method could also recognize faults in other similar areas with harsh conditions that are not feasible for ground-based surveys, such as forests and glaciers.

Sea clutter suppression algorithm in spatial domain based on singular value decomposition

In order to detect the targets in the first-order sea clutter spectrum of high frequency surface wave radar (HFSWR), a method based on singular value decomposition (SVD) is proposed to suppress sea clutter in spatial domain. The sea clutter suppression method is based on the strong correlation of sea clutter, thus we take the adjacent range bin as the reference. Then the sea clutter subspace and noise subspace in the spatial domain is estimated by singular value decomposition of its array covariance matrix. Finally, we subtract its projection in the sea clutter subspace from the array signal with the orthogonality of subspace, and achieves the purpose of suppressing sea clutter in the spatial domain. Compared with the existing methods of sea clutter suppression in spatial domain, there is no need to know the azimuth of sea clutter in advance. The subspace is estimated by singular value decomposition of its array covariance matrix, which makes it easier and more accurate to estimate the sea clutter subspace and noise subspace. The result shows that the method improves the signal to clutter plus noise ratio (SCNR), which is benefit to target detection.

An improved multi-focus image fusion algorithm based on multi-scale weighted focus measure

This paper focuses on developing an improved multi-focus image fusion (MFIF) algorithm. Existing spatial domain algorithms dependent on the obtained fusion decision map still lead to unexpected ghosting, blurred, edges as well as blocking effects such that the visual effect of image fusion is seriously degraded. To overcome these shortages, an improved MFIF algorithm is developed with the help of a novel multi-scale weighted focus measure and a decision map optimization technique. First, a novel multi-scale measurement template is designed in order to effectively extract the gradient information of rich texture regions, smooth regions as well as transitional regions between the aforementioned regions simultaneously. Then, an improved calculation scheme of the focus score matrix is designed based on the weighted sum of the focus measure maps in each region window centered on a concerned pixel, under which the advantage of pixel-by-pixel weighting is employed. In what follows, an initial decision map is obtained in light of the focus score matrix combined with threshold filtering, which is employed to eliminate the small isolated regions caused by some misclassified pixels. Furthermore, an accurate decision map is received with the help of the optimization capability of guided filtering to avoid edge unexpected artificial textures. In comparison with block-based fusion algorithms, our algorithm developed in this paper extracts the focus regions pixel-by-pixel, thereby helping to reduce the blocking effects that appear in the fusion image. Finally, some intensive comparison analysis based on common datasets is performed to verify the superiority over state-of-the-art methods in both visual qualitative and quantitative evaluations.

Spatial Interpolators for Intra-Frame Resampling of SAR Videos: A Comparative Study Using Real-Time HD, Medical and Radar Data

Background:Real-time video coding is a very interesting area of research with extensive applications into remote sensing and medical imaging. Many research works and multimedia standards for this purpose have been developed. Some processing ideas in the area are focused on second-step (additional) compression of videos coded by existing standards like MPEG 4.14.Materials and Methods:In this article, an evaluation of some techniques with different complexity orders for video compression problem is performed. All compared techniques are based on interpolation algorithms in spatial domain. In details, the acquired data is according to four different interpolators in terms of computational complexity including fixed weights quartered interpolation (FWQI) technique, Nearest Neighbor (NN), Bi-Linear (BL) and Cubic Cnvolution (CC) interpolators. They are used for the compression of some HD color videos in real-time applications, real frames of video synthetic aperture radar (video SAR or ViSAR) and a high resolution medical sample.Results:Comparative results are also described for three different metrics including two reference- based Quality Assessment (QA) measures and an edge preservation factor to achieve a general perception of various dimensions of the mentioned problem.Conclusion:Comparisons show that there is a decidable trade-off among video codecs in terms of more similarity to a reference, preserving high frequency edge information and having low computational complexity.

Design and methodology of algorithm for the enhancement of breast tumor images

Breast cancer is the second largest disease in the world having high mortality rate among ladies. Breast image enhancement is required for the foundation of high accuracy segmentation. Several features are extracted from the ROI of mammogram in order to detect the cancer tumor in the early stage. Ultrasound breast images have the speckle noise during the capturing of image. In mammogram images also, the noises like Guassian, Normal and Poisson are introduced. There are no proper enhancements algorithms are designed to enhance the images for the preprocessing of the images. The image enhancement is broadly categorized into two different domains such as spatial and frequency. The spatial domain algorithms will smoothen the image quality and frequency domain algorithms will sharpen the image. Till today there is no concrete work on suitable algorithm methods using different domains of enhancement. The research work has been implemented in two stages. In the first stage we are identifying various noises in the mammogram and ultrasound images. In second stage the algorithms like mean, median, Homomorphic, Weiner, Modified Gabor, Anisoid diffusion filter, Contrast limited Adaptive Histogram equalization (CLAHE), Bilateral filter and Guided filter are worked out. Among all these filters we modified Gabor filter that clearly highlights the portion of the tumor. For all these filters Mean Square Error (MSE), RMSE, PSNR, VAR, SSIM, SD are computed. The best PSNR is obtained for the modified Gabor filter.

A combined domain watermarking algorithm of color image

At present, some watermarking techniques have been proposed to protect the copyright of image either in frequency domain or in spatial domain. Designing a combined-domain watermarking algorithm is a difficult problem, which not only has short running time of spatial-domain watermarking algorithm but also has strong robustness of transform-domain watermarking algorithm. In this paper, a novel color image watermarking algorithm in spatial domain combining the principle of discrete Wavelet transform (DWT) is presented to embed color watermark image into color carrier image. At first, how to get the first low-frequency coefficient of DWT in spatial domain is analyzed. Then, the changing quantity of the first low-frequency coefficient in spatial domain is calculated by the presented algorithm. According to the presented features of the first low-frequency coefficient, the processes of embedding watermark and extracting watermark information are finished in spatial-domain instead of DWT. This presented algorithm has the following two innovations: (i) the first low-frequency coefficient of DWT is computed by image pixels in spatial domain; (ii) the presented algorithm is as robust as the transform-domain watermarking methods, and it is as rapid as the spatial-domain watermarking methods. The simulation data shows the presented algorithm has good watermarking performances as respect to visual imperceptibility, robustness, payload, and running time.

A New Spatial Domain Filter for Impulse Noise Removal with Improved Accuracy Based on Multiple Conditions

Numerous filtering methods are proposed for Impulse noise removal, it is an important task in the field of image restoration. The familiar spatial domain algorithm to remove impulse noise is Standard Median Filter (SMF). Most of the existing algorithms are based on median filtering and recent algorithms are Modified Hybrid Median Filter (MHMF) and New Modified Hybrid Median Filter (NMHMF). These two are worked up to 20% noise density. In this paper proposed a new` algorithm for impulse noise removal above 20% noise density conditions with different samples of images. The implementation of proposed method compares with known existing methods by comparing Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).

Enhanced Least Significant Bit Replacement Algorithm in Spatial Domain of Steganography Using Character Sequence Optimization

Text Steganography has become a dominant research field in information sharing domain and many researches are being conducted to strengthen this area. Researches around the amount of secret message that could be stored in a given cover image is always critical for any steganography technique used to share the secret text. This research paper proposes an enhanced Least Significant Bit (eLSB) embedding technique in steganography, through which the quality of cover image is improved, when compared to typical LSB algorithm used in steganography. The proposed method employs in spatial domain and it does the secret message encoding in two phases. The first phase generates the metadata and embeds the header information in first few bytes of cover image and then the following phase takes care of processing secret message and storing the secret message in cover image using an optimized way, which is possible through analyzing secret text’s character sequences. Proposed work results into occupying lesser space for the given secret text in cover image and hence leads to the better stego image quality than existing LSB algorithms. As the algorithm works on optimizing secret message during embedding phase itself, this technique enables high capacity embedding rate, additional security due to secret message preprocessing and enhanced cover image quality. The results are compared with LSB algorithm and compared to Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Root Mean Square Error (RMSE) values to prove the proposed algorithm performs better on secret text embedding in cover image.

Rapid-Precision Position Measurement of Linear Motor Mover Based on Joint Spatial Phase Method

In this article, a rapid and precise position measurement method based on joint spatial phase is studied for linear motors. First, a low-cost mover position measurement system is designed on the basis of motion characters of linear motors. The target image is an aperiodic fence stripe. The image sequences with the movement of the mover are recorded quickly by linear charge coupled device (CCD) camera. Second, spatial domain algorithm is introduced for the rough registration, and according to the registration results, the overlapping regions are extracted to form two new images, and then the improved phase difference method is used to get the accurate displacement of the two new images. The actual mover displacement is finally calculated by the calibration coefficient of the system. Simulation and experimental results show that this method proposed takes approximately 2 ms on average, and the measurement accuracy reaches 0.02 mm.

Residual Motion Error Correction with Backprojection Multisquint Algorithm for Airborne Synthetic Aperture Radar Interferometry.

For airborne interferometric synthetic aperture radar (InSAR) data processing, it is essential to achieve precise motion compensation to obtain high-quality digital elevation models (DEMs). In this paper, a novel InSAR motion compensation method is developed, which combines the backprojection (BP) focusing and the multisquint (MSQ) technique. The algorithm is two-fold. For SAR image focusing, BP algorithm is applied to fully use the navigation information. Additionally, an explicit mathematical expression of residual motion error (RME) in the BP image is derived, which paves a way to integrating the MSQ algorithm in the azimuth spatial wavenumber domain for a refined RME correction. It is revealed that the proposed backprojection multisquint (BP-MSQ) algorithm exploits the motion error correction advantages of BP and MSQ simultaneously, which leads to significant improvements of InSAR image quality. Simulation and real data experiments are employed to illustrate the effectiveness of the proposed algorithm.

New Method of Image Steganography Based on Particle Swarm Optimization Algorithm in Spatial Domain for High Embedding Capacity

Steganography is a form of technology utilised to safeguard secret data during communication in addition to data repository. Numerous researchers have endeavoured to enhance the performance of steganography techniques through the development of an effective algorithm for the selection of the optimal pixel location within the host image for the concealment of secret bits, for the enhancement of the embedding capacity of the secret data, and for maintaining the visual quality of the host image (stego image) in an accepted rate after the concealment of the secret data. Therefore, steganography is perceived as a challenging task. Thus, the current study proposes a new technique for image steganography based on particle swarm optimisation (PSO) algorithm by using pixel selection for the concealment of a secret image in spatial domain, for the purpose of high embedment capacity. The stego possesses a high level of resistance against a steganalytic attack due to the security provided via image steganography. The function of PSO algorithm is to choose an optimal pixel in grey scale host image for the concealment of secret bits, as the PSO has the ability to achieve an efficient fitness calculation that depends on the cost matrix by dividing the host and secret images into four parts. First of all, the secret bits are modified, which are then embedded within the host image. Several locations in the host image are determined through the order of scanning the host pixels and starting point of the scanning for better least significant bits LSBs of each pixel. The PSO algorithm was utilised to ascertain the ideal initiating point and scanning order. Experimental results show that (1) the average peak signal to noise ratio PSNR value in the benchmark technique based on genetic algorithm for five standard stego images is 45.13%, whereas the result obtained from the recommended technique is 56.60%. (2) The proposed technique has an advantage over the benchmark with a percentage of 33.34%, which encompasses all associated issues within the checklist scenario. Therefore, the performance of the recommended technique is superior over existing techniques.

Multiple‐parameter fractional quaternion Fourier transform and its application in colour image encryption

In this study, by using the quaternion algebra, multiple-parameter fractional quaternion Fourier transform (MPFrQFT) is proposed to generalise the conventional multiple-parameter fractional Fourier transform (MPFrFT) to quaternion signal processing in a holistic manner. First, the new transform MPFrQFT and its inverse transform are defined. An efficient discrete implementation method of MPFrQFT is then proposed, in which the relationship between MPFrQFT and MPFrFT of four components is utilised for a quaternion signal. Finally, a new colour image encryption algorithm based on the proposed MPFrQFT and the double random phase encoding technique is proposed to evaluate the performance of the proposed MPFrQFT. Experimental results demonstrate that: (i) the computational time of the proposed implementation method is almost a half of the direct method's time; (ii) the proposed MPFrQFT-based encryption algorithm has an overall better performance than eight compared algorithms in security test and robustness test: it is more secure than the compared frequency-based algorithms due to the larger key space and the more sensitive key ‘transform orders’; it is also more robust than the compared spatial-domain algorithms.

Universal secret payload location identification in spatial LSB stego images

Locating steganographic payload is an important aspect of active steganalysis which deals with finding the pixel locations of the embedded secret message (payload) in the stego image. The residual-based payload location detectors are well suited for this purpose. In this paper, an improved universal, blind method of precisely identifying locations in the spatial domain using enhanced weighted residuals with only a few known stego images is proposed. This is done by estimating the cover from the available stego image using the proposed novel locally weighted bivariate shrinkage function in the transform domain. The enhanced weighted residuals ensure that the proposed method is universally applicable for detecting payload locations in spatial least significant bit stego images. The added advantage of the proposed method is that it does not require any prior knowledge of the cover source or the embedding algorithm to determine the exact locations. Experiments conducted on five spatial LSB domain algorithms show that the payload locations can be precisely estimated with a minimum accuracy of 90% with 100 known stego images.

Detection of non-small cell lung cancer cells based on microfluidic polarization microscopic image analysis.

In early diagnosis of lung cancer, a polarization microscopy is a powerful tool to obtain the optical information of biological tissues. In this paper, a new microfluidic polarization imaging and analysis method was proposed for the detection and classification of cancer-associated fibroblasts and the two kinds of non-small cell lung cancer cells, A549 and H322. A polarizing microscopy system was constructed based on a commercial microscope to obtain 3*3 Mueller matrix of cells. Based on the Muller matrix decomposition algorithm and analysis in spatial domain and frequency domain, appropriate classification parameters were selected for the characterization of different polarization characteristics of cells. Finally, the logistic regression models based on machine learning were applied to determine optimal feature parameters and classify cells. This method integrated the morphological information of the cells, and the polarization characteristics of the cells in different polarization states. It is for the first time that the polarization microscopic image analysis method has been applied to the detection and classification of non-small cell lung cancer cells. The results show that the presented microfluidic polarization microscopic image analysis method could classify cells effectively. Compared with the Muller matrix measurement and calculation methods, the method proposed in this paper was greatly simplified in both the acquisition of polarized images and the analysis and processing of polarized images.

Multi-focus image fusion algorithm based on focus detection in spatial and NSCT domain.

Multi-focus image fusion is an effective approach to obtain the all-in-focus image. Focus detection is the key issue of multi-focus image fusion. Aiming at the shortcoming of spatial domain and transform domain algorithms for multi-focus image fusion, a novel multi-focus image fusion algorithm is proposed by combing focus detection in spatial domain and non-subsampled contourlet transform (NSCT) domain. At first, the focused pixels are detected by the sum-modified-Laplacian algorithm in spatial domain. At the same time, the focus detection method is proposed in NSCT domain, namely by MPCNN and voting fusion methods for high-frequency subbands of NSCT. Then, the morphological operation is utilized to correct the focus detection results in spatial domain and NSCT domain. At last, synthesis of detection results is implemented and the fused image can be obtained. Experimental results verified that the proposed algorithm outperformed some state-of-the-art fusion algorithms in terms of both subjective observation and objective evaluations.