Merging Images Research Articles

VLA and ALMA observations of the lensed radio-quiet quasar SDSS J0924+0219: a molecular structure in a 3 μJy radio source

ABSTRACT We present Karl G. Jansky Very Large Array (VLA) and Atacama Large Millimetre Array (ALMA) observations of SDSS J0924+0219, a z = 1.524 radio-quiet lensed quasar with an intrinsic radio flux density of about 3 $\, \mu$Jy. The four lensed images are clearly detected in the radio continuum and the CO(5–4) line, whose centroid is at z = 1.5254 ± 0.0001, with a marginal detection in the submillimetre continuum. The molecular gas displays ordered motion, in a structure approximately 1–2.5 kpc in physical extent, with typical velocities of 50–100 km s−1. Our results are consistent with the radio emission being emitted from the same region, but not with a point source of radio emission. SDSS J0924+0219 shows an extreme anomaly in the flux ratios of the two merging images in the optical continuum and broad emission lines, suggesting the influence of microlensing by stars in the lensing galaxy. We find the flux ratio in the radio, submillimetre continuum and CO lines to be slightly greater than 1 but much less than that in the optical, which can be reproduced with a smooth galaxy mass model and an extended source. Our results, supported by a microlensing simulation, suggest that the most likely explanation for the optical flux anomaly is indeed microlensing.

FastText-Based Local Feature Visualization Algorithm for Merged Image-Based Malware Classification Framework for Cyber Security and Cyber Defense

The importance of cybersecurity has recently been increasing. A malware coder writes malware into normal executable files. A computer is more likely to be infected by malware when users have easy access to various executables. Malware is considered as the starting point for cyber-attacks; thus, the timely detection, classification and blocking of malware are important. Malware visualization is a method for detecting or classifying malware. A global image is visualized through binaries extracted from malware. The overall structure and behavior of malware are considered when global images are utilized. However, the visualization of obfuscated malware is tough, owing to the difficulties encountered when extracting local features. This paper proposes a merged image-based malware classification framework that includes local feature visualization, global image-based local feature visualization, and global and local image merging methods. This study introduces a fastText-based local feature visualization method: First, local features such as opcodes and API function names are extracted from the malware; second, important local features in each malware family are selected via the term frequency inverse document frequency algorithm; third, the fastText model embeds the selected local features; finally, the embedded local features are visualized through a normalization process. Malware classification based on the proposed method using the Microsoft Malware Classification Challenge dataset was experimentally verified. The accuracy of the proposed method was approximately 99.65%, which is 2.18% higher than that of another contemporary global image-based approach.

Development of an Automated Image Analyzer for Microvessel Density Measurement in Bone Marrow Biopsies.

Angiogenesis is important for the proliferation and survival of multiple myeloma (MM) cells. Bone marrow (BM) microvessel density (MVD) is a useful marker of angiogenesis and an increase in MVD can be used as a marker of poor prognosis in MM patients. We developed an automated image analyzer to assess MVD from images of BM biopsies stained with anti-CD34 antibodies using two color models. MVD was calculated by merging images from the red and hue channels after eliminating non-microvessels. The analyzer results were compared with those obtained by two experienced hematopathologists in a blinded manner using the 84 BM samples of MM patients. Manual assessment of the MVD by two hematopathologists yielded mean±SD values of 19.4±11.8 and 20.0±11.8. The analyzer generated a mean±SD of 19.5±11.2. The intraclass correlation coefficient (ICC) and Bland-Altman plot of the MVD results demonstrated very good agreement between the automated image analyzer and both hematopathologists (ICC=0.893 [0.840–0.929] and ICC=0.906 [0.859–0.938]). This automated analyzer can provide time- and labor-saving benefits with more objective results in hematology laboratories.

Multi-Focus Color Image Fusion Algorithm Based on Super-Resolution Reconstruction and Focused Area Detection

Multi-focus image fusion is an image processing that generates an integrated image by merging multiple images from different focus area in the same scene. For most fusion methods, the detection of the focus area is a critical step. In this paper, we propose a multi-focus image fusion algorithm based on a dual convolutional neural network (DualCNN), in which the focus area is detected from super-resolved images. Firstly, the source image is input into a DualCNN to restore the details and structure from its super-resolved image, as well as to improve the contrast of the source image. Secondly, the bilateral filter is used to reduce noise on the fused image, and the guided filter is used to detect the focus area of the image and refine the decision map. Finally, the fused image is obtained by weighting the source image according to the decision map. Experimental results show that our algorithm can well retain image details and maintain spatial consistency. Compared with existing methods in multiple groups of experiments, our algorithm can achieve better visual perception according to subjective evaluation and objective indexes.

Research on the optimal design of municipal roads based on genetic algorithms

In order to improve the rationality of the design of municipal roads and realise the optimal planning and design of municipal roads, an empirical road optimisation design method based on genetic algorithm is proposed. In the sample of remote sensing image of municipal road, by solving the zero uniform ergodic characteristic and logical difference variable scale characteristic of objective function, the analysis and design of complex urban road pattern is realised, according to the principle of pixel correlation, genetic algorithm is used to optimise the design of municipal roads, and the image merging planning analysis model of municipal roads is constructed. The feature classification of municipal roads is carried out by genetic method, and the cross edge contour feature segmentation method of municipal roads is adopted. Optimise planning and network design of municipal roads. The simulation results show that this method can improve the rational layout of municipal road planning and improve the spatial planning capacity and traffic capacity of municipal roads.

Research on the optimal design of municipal roads based on genetic algorithms

In order to improve the rationality of the design of municipal roads and realise the optimal planning and design of municipal roads, an empirical road optimisation design method based on genetic algorithm is proposed. In the sample of remote sensing image of municipal road, by solving the zero uniform ergodic characteristic and logical difference variable scale characteristic of objective function, the analysis and design of complex urban road pattern is realised, according to the principle of pixel correlation, genetic algorithm is used to optimise the design of municipal roads, and the image merging planning analysis model of municipal roads is constructed. The feature classification of municipal roads is carried out by genetic method, and the cross edge contour feature segmentation method of municipal roads is adopted. Optimise planning and network design of municipal roads. The simulation results show that this method can improve the rational layout of municipal road planning and improve the spatial planning capacity and traffic capacity of municipal roads.

Laterally Extended Field of View Imaging without Geometric Distortion using Image Stitching in Open-Configuration MRI

Open-configuration MRI for easy patient access causes a restricted imaging field of view and geometric distortion resulting in reduced reliability of the quantitation of tissue segmentation. The method proposed in this study is use of MR image stitching, which is merging divided isocentric images without geometric distortion of the same object through the table moving laterally. This study was performed using an open 0.32 T MRI system. MR imaging with lateral shift of the scanner table was conducted using an AAPM MRI Phantom. The composite image produced through the sequential generation of the same object at different table positions has been generated using the image stitching technique. Image stitching consisted of tile configurations using optimized global registration and then seamlessly blending the aligned tiles. The overall image stitching procedure reduced the geometric distortion of image at off-center regions, and the FOV in the stitched image was extended 40% laterally relative to conventional isocenter image. The maximum distortion value was about 16.8 mm in the conventional image and was reduced to < 1.7 mm after the seamless image stitching procedure. Laterally extended FOV without geometric distortion was successfully acquired using seamless image stitching procedure. These methods are beneficial towards defining tissue segmentation in obese patient with large field of view in open MRI.

Complete Scene Reconstruction by Merging Images and Laser Scans

Image based modeling and laser scanning are two commonly used approaches in large-scale architectural scene reconstruction nowadays. In order to generate a complete scene reconstruction, an effective way is to completely cover the scene using ground and aerial images, supplemented by laser scanning on certain regions with low textures and complicated structures. Thus, the key issue is to accurately calibrate cameras and register laser scans in a unified framework. To this end, we proposed a three-step pipeline for complete scene reconstruction by merging images and laser scans. First, images are captured around the architecture in a multiview and multiscale way and are feed into a structure-from-motion (SfM) pipeline to generate SfM points. Then, based on the SfM result, the laser scanning locations are automatically planned by considering textural richness, structural complexity of the scene and spatial layout of the laser scans. Finally, the images and laser scans are accurately merged in a coarse-to-fine manner. Experimental evaluations on two ancient Chinese architecture datasets demonstrate the effectiveness of our proposed complete scene reconstruction pipeline.

Fusion of multi‐modal lumbar spine images using Kekre's hybrid wavelet transform

Image fusion is the process of merging multiple images to generate a single image called ‘fused image’ which is more informative than input images in terms of human perception and machine processing. In medical applications, images of the same or different modalities are fused to generate a new image which helps clinicians in reliable and accurate diagnosis. Fused image of mono-modal medical images is used to see pre- and post-operative results. Multi-modal medical images are fused for treatment or surgical planning. In this study, the authors have focused on the fusion of lumbar spine images of two completely different modalities: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). CT provides bony details whereas MR provides soft tissue details. Since the two images are captured using two different machines, these images need to be strictly aligned with each other before fusing. Kekre's Hybrid wavelet transform (KHWT) is used to fuse registered images using combinations of six different orthogonal transforms with four different transform sizes. It is compared with five other fusion methods in qualitative and quantitative ways. The overall comparison indicates that the fused image generated using KHWT is better than input images in terms of content, quality and contrast.

Merging Preclinical EPR Tomography with other Imaging Techniques

This paper presents a survey of electron paramagnetic resonance (EPR) image registration. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints and/or different techniques. EPR-imaging (EPRI) techniques belong to the functional-imaging modalities and therefore suffer from a lack of anatomical reference which is mandatory in preclinical imaging. For this reason, it is necessary to merging EPR images with other modalities which allow for obtaining anatomy images. Methodological analysis and review of the literature were done, providing a summary for developing a good foundation for research study in this field which is crucial in understanding the existing levels of knowledge. Out of these considerations, the aim of this paper is to enhance the scientific community’s understanding of the current status of research in EPR preclinical image registration and also communicate to them the contribution of this research in the field of image processing.

Adaptive Batch Size Image Merging Steganography and Quantized Gaussian Image Steganography

In digital image steganography, the statistical model of an image is essential for hiding data in less detectable regions and achieving better security. This has been addressed in the literature where different cost-based and statistical model-based approaches were proposed. However, due to the usage of heuristically defined distortions and non-constrained message models, resulting in numerically solvable equations, there is no closed-form expression for security as a function of payload. The closed-form expression is crucial for a better insight into image steganography problem and also improving performance of batch steganography algorithms. Here, we develop a statistical framework for image steganography in which the cover and the stego messages are modeled as multivariate Gaussian random variables. We propose a novel Gaussian embedding model by maximizing the detection error of the most common optimal detectors within the adopted statistical model. Furthermore, we extend the formulation to cost-based steganography, resulting in a universal embedding scheme that improves empirical results of current cost-based and statistical model-based approaches. This methodology and its presented solution, by reason of assuming a continuous hidden message, remains the same for any embedding scenario. Afterward, the closed-form detection error is derived within the adopted model for image steganography and it is extended to batch steganography. Thus, we introduce Adaptive Batch size Image Merging steganographer, AdaBIM , and mathematically prove it outperforms the state-of-the-art batch steganography method and further verify its superiority by experiments.

Melhor livro para criança em 2016 – Inês, de Roger Mello e Mariana Massarani – o que diz a crítica?

El objetivo es comprender qué concepciones de niño y lectura atravesan los discursos de los lectores votantes que premiaron a Inés, de Roger Mello y Mariana Massarani como el mejor libro para ni-ños por la Fundación Nacional del Libro Infantil y Juvenil en 2016. Bajo la óptica del Análisis del Discurso foucaultiana se aborda el concepto de enunciado y formación discursiva, considerando estos presupuestos: i) esos lectores son considerados sujetos competentes para asumir la palabra en relación a una literatura de calidad ii) sus opiniones son acontecimientos discursivos; y iii) todo enunciado tiene una materialidad. Elaborar opiniones es un modo de textualizar impresiones, sen-tidos. La actividad discursiva puede reintegrar conocimientos en los que la tónica es despertar la mirada múltiple del lector. En Inés, palabra e imagen se funden, complementan e incorporan datos de la infancia por el lector adulto. Discurso atravesado por relaciones de saber y poder.

Multifocus Image Fusion Using Wavelet-Domain-Based Deep CNN.

Multifocus image fusion is the merging of images of the same scene and having multiple different foci into one all-focus image. Most existing fusion algorithms extract high-frequency information by designing local filters and then adopt different fusion rules to obtain the fused images. In this paper, a wavelet is used for multiscale decomposition of the source and fusion images to obtain high-frequency and low-frequency images. To obtain clearer and complete fusion images, this paper uses a deep convolutional neural network to learn the direct mapping between the high-frequency and low-frequency images of the source and fusion images. In this paper, high-frequency and low-frequency images are used to train two convolutional networks to encode the high-frequency and low-frequency images of the source and fusion images. The experimental results show that the method proposed in this paper can obtain a satisfactory fusion image, which is superior to that obtained by some advanced image fusion algorithms in terms of both visual and objective evaluations.

Improved scheduling algorithm for signal processing in asynchronous distributed ultrasonic total-focusing-method system.

Compared to the conventional ultrasonic phased-array system, a large-element phased-array system employing the total focusing method (TFM) can yield improved image resolution and accuracy, providing more flexible scanning methods and image merging functionality. In order to meet various forms of ultrasonic multi-group scanning, an architecture for multi-group scan integration called the “asynchronous distributed ultrasonic TFM system” is proposed, and a novel scheduling algorithm called “the sum of start time and processing time adjacent (SSPA) algorithm” is presented. The architecture adds a focus and group scheduler (FGS) and signal processing scheduler (SPS) to the traditional ultrasonic phased array system and constructs a signal processing arbitration (SPA) with several signal processing modules (SPMs). The FGS provides the focus parameters, pixel memory range, and number of pixels in each group. The SPS controls the SPA for the ultrasonic scanning data obtained from the elements, with SPM-sharing output data; hence, the optimal priority order and SPM assignment are realized, enabling switching of reading operations among the first-in−first-out memories for signal processing and minimal time-slot waiting. The SSPA algorithm is used to solve the job-shop scheduling problem with start time, which considers the processing time and start time, in order to reduce the time slot after each scheduling using adjacent operations. Therefore, the architecture enhances the flexibility of the multi-group scan, and this algorithm decreases the makespan, achieving higher efficiency compared to conventional scheduling algorithms. The reliability and validity of the algorithm are substantiated after its implementation using FPGA technology. The SPM utilization rate and the real-time performance of the ultrasonic TFM are improved. Thus, the proposed algorithm and architecture have considerable potential application in multi-sensor systems.

Studying the effect of lossy compression and image fusion on image classification

Nowadays, the remotely sensed images are of huge sizes that require the implementation of compression technique to be easily stored on the internet. In addition, image fusion which is the merging of panchromatic and multispectral images to generate a single image with high spatial and spectral resolutions is required to increase the information in the resulted image. The purpose of this paper is to study the effect of image compression and fusion techniques on the classification accuracy. In this study, two pan and mul Geo-eye images covering an area of Cape Town, South Africa were registered and fused using different fusion techniques. The fused image with the superior accuracy was then compressed with various compression ratios ranging from 1:10 to 1:100. Then, the compressed fused images were classified using Maximum Likelihood Classification and Artificial Neural Network Classification techniques. Finally, the confusion matrices of the classified images were generated and evaluated to determine the effect of compression and fusion techniques on the accuracy of the classification process.

FUSION OF HYPERSPECTRAL AND MULTISPECTRAL IMAGERY WITH REGRESSION KRIGING AND THE LULU OPERATORS; A COMPARISON

Abstract. In this digital world, there is a large requirement of high resolution satellite image. Images at a low resolution may contain relevant information that has to be integrated with the high resolution image to obtain the required information. This is being fulfilled by image fusion. Image fusion is merging of different resolution images into a single image. The output image contains more information, as the information is integrated from both the images Image fusion was conducted with two different algorithms: regression kriging and the LULU operators. First, regression Kriging estimates the value of a dependent variable at unsampled location with the help of auxiliary variables. Here we used regression Kriging with the Hyperion image band as the response variables and the LISS III image bands are the explanatory variables. The fused image thus has the spectral variables from Hyperion image and the spatial variables from the LISS III image. Second, the LULU operator is an image processing methods that can be used as well in image fusion technique. Here we explored to fuse the Hyperion and LISS III image. The LULU operators work in three stages of the process, viz the decomposition stage, the fusion and the reconstruction stage. Quality aspects of the fused image for both techniques have been compared for spectral quality (correlation) and spatial quality (entropy). The study concludes that the quality of the fused image obtained with regression kriging is better than that obtained with the LULU operator.

Fusion of Unmanned Aerial Vehicle Panchromatic and Hyperspectral Images Combining Joint Skewness-Kurtosis Figures and a Non-Subsampled Contourlet Transform.

To obtain fine and potential features, a highly informative fused image created by merging multiple images is usually required. In our study, a novel fusion algorithm called JSKF-NSCT is proposed for fusing panchromatic (PAN) and hyperspectral (HS) images by combining the joint skewness-kurtosis figure (JSKF) and the non-subsampled contourlet transform (NSCT). The JSKF model is used first to derive the three most sensitive bands from the original HS image according to the product of the skewness and the kurtosis coefficients of each band. Afterwards, an intensity-hue-saturation (IHS) transform is used to obtain the luminance component I of the produced false-colour image consisting of the above three bands. Then the NSCT method is used to decompose component I of the false-colour image and the PAN image. The weight-selection rule based on the regional energy is adopted to acquire the low-frequency coefficients and the correlation between the central pixel and its surrounding pixels is used to select the high-frequency coefficients. Finally, the fused image is obtained by applying an IHS inverse transform and an inverse NSCT transform. The unmanned aerial vehicle (UAV) HS and PAN images under low- and high-vegetation coverage of wheat at the flag leaf stage (Stage I) and the grain filling stage (Stage II) are used as the sample data sources. The fusion results are comparatively validated using spatial (entropy, standard deviation, average gradient and mean) and spectral (normalised difference vegetation, NDVI, and leaf area index, LAI) assessments. Additional comparative studies using anomaly detection and pixel clustering also demonstrate that the proposed method outperforms other methods. They show that the algorithm reported herein can better preserve the original spatial and spectral characteristics of the two types of images to be fused and is more stable than IHS, principal components analysis (PCA), non-negative matrix factorization (NMF) and Gram-Schmidt (GS).

Merging images with different central frequencies reduces banding artifacts in balanced steady-state free precession magnetic resonance cisternography.

PurposeThe aim of this study was to evaluate the utility of merged balanced steady‐state free precession (bSSFP) magnetic resonance cisternography images.Materials and MethodsTwenty ears of 10 healthy volunteers (six men, four women; mean age ± standard deviation, 26.7 ± 1.6 yr) and 10 patients (two men, eight women; mean age, 46.3 ± 10.9 yr) with neoplasm around the sella turcica were included. Two different devices (A and B) were used to confirm the versatility of our method for MR devices with different local magnetic field homogeneity. Images with different central frequencies (±10, ±20, ±30, ±40, and ±50 Hz) were merged with the maximum magnitude of corresponding pixels from the images acquired using both devices. Two neuroradiologists visually graded the image quality of 11 sites in the inner ear and three sites around the sella turcica (scale: 0–2) and compared the quality with that of the corresponding basic image (0 Hz).ResultsThe image quality was better in merged images of the vestibule, superior semicircular canal (SCC), posterior SCC, and horizontal SCC (P = 0.005 to 0.020 mainly at ±40 and ±50 Hz on devices A and B), as well as in merged images of the sella turcica and right cavernous sinus (±50 Hz, P = 0.003 and 0.020 on device B, respectively), than it was in the corresponding basic images.ConclusionsThe maximum magnitude merging of images with different central frequencies makes it possible to reduce banding artifacts on bSSFP images without the need for special pulse sequences and image processing programs.

GPU based techniques for deep image merging

Deep images store multiple fragments perpixel, each of which includes colour and depth, unlike traditional 2D flat images which store only a single colour value and possibly a depth value. Recently, deep images have found use in an increasing number of applications, including ones using transparency and compositing. A step in compositing deep images requires merging per-pixel fragment lists in depth order; little work has so far been presented on fast approaches.This paper explores GPU based merging of deep images using different memory layouts for fragment lists: linked lists, linearised arrays, and interleaved arrays. We also report performance improvements using techniques which leverage GPU memory hierarchy by processing blocks of fragment data using fast registers, following similar techniques used to improve performance of transparency rendering. We report results for compositing from two deep images or saving the resulting deep image before compositing, as well as for an iterated pairwise merge of multiple deep images. Our results show a 2 to 6 fold improvement by combining efficient memory layout with fast register based merging.

Corneal Endothelial Cell Segmentation by Classifier-Driven Merging of Oversegmented Images.

Corneal endothelium images obtained by in vivo specular microscopy provide important information to assess the health status of the cornea. Estimation of clinical parameters, such as cell density, polymegethism, and pleomorphism, requires accurate cell segmentation. State-of-the-art techniques to automatically segment the endothelium are error-prone when applied to images with low contrast and/or large variation in cell size. Here, we propose an automatic method to segment the endothelium. Starting with an oversegmented image comprised of superpixels obtained from a stochastic watershed segmentation, the proposed method uses intensity and shape information of the superpixels to identify and merge those that constitute a cell, using support vector machines. We evaluated the automatic segmentation on a data set of in vivo specular microscopy images (Topcon SP-1P), obtaining 95.8% correctly merged cells and 2.0% undersegmented cells. We also evaluated the parameter estimation against the results of the vendor's built-in software, obtaining a statistically significant better precision in all parameters and a similar or better accuracy. The parameter estimation was also evaluated on three other data sets from different imaging modalities (confocal microscopy, phase-contrast microscopy, and fluorescence confocal microscopy) and tissue types (ex vivo corneal endothelium and retinal pigment epithelium). In comparison with the estimates of the data sets' authors, we achieved statistically significant better accuracy and precision in all parameters except pleomorphism, where a similar accuracy and precision were obtained.