Multichannel Image Processing Research Articles

Homomorphic filtering in digital multichannel image processing

Purpose. The purpose of this article is to develop a preprocessing method for digital multispectral remote sensing images obtained through optical and infrared means in the electromagnetic spectrum. The method aims to ensure invariance with respect to positional formation conditions that determine spatial and radiometric resolution. By implementing homomorphic filtering in this method, we can significantly increase the informative value of processed imagery. Methodology. The problem solving, including the development of the spatial and radiometric resolution increase ways for multispectral geospatial data are based on the methods of brightness spatial distribution fusion, methods of data dimension reduction, de-correlation techniques and geometric correction of image spatial distributions. Findings. The method of preprocessing digital remote sensing data has been developed, which is a component of the methodology for identifying geometric shapes (GS) of objects in multi-channel aerospace images, allowing for a significant improvement in their recognition efficiency when noise is present. Originality. The method of preprocessing photogrammetric scenes using homomorphic filtering to enhance their informational significance is proposed. The method ensures invariance to positional conditions of fixation, improves the accuracy of further recognition, eliminates the drawbacks of known methods associated with the existence of parametric uncertainty dependence, the features of fixation of species information, low values of information indices of synthesized images, and computational process peculiarities. Practical value. Practical value is consists in improving of identification accuracy of objects GS in digital geospatial data, in significant increasing of raster multispectral images information value and in rising of automated image processing efficiency. The use of the method can greatly enhance the value and usefulness of multispectral photogrammetric images in a wide range of applications, from environmental monitoring to urban planning.

An improved novel quantum image representation and its experimental test on IBM quantum experience

Quantum image representation (QIR) is a necessary part of quantum image processing (QIP) and plays an important role in quantum information processing. To address the problems that NCQI cannot handle images with inconsistent horizontal and vertical position sizes and multi-channel image processing, an improved color digital image quantum representation (INCQI) model based on NCQI is proposed in this paper. The INCQI model can process color images and facilitate multi-channel quantum image transformations and transparency information processing of images using auxiliary quantum bits. In addition, the quantum image control circuit was designed based on INCQI. And quantum image preparation experiments were conducted on IBM Quantum Experience (IBMQ) to verify the feasibility and effectiveness of INCQI quantum image preparation. The prepared image information was obtained by quantum measurement in the experiment, and the visualization of quantum information was successfully realized. The research in this paper has some reference value for the research related to QIP.

High-Speed Lightweight Ship Detection Algorithm Based on YOLO-V4 for Three-Channels RGB SAR Image

Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.

A versatile toolbox for semi-automatic cell-by-cell object-based colocalization analysis

Differential fluorescence labeling and multi-fluorescence imaging followed by colocalization analysis is commonly used to investigate cellular heterogeneity in situ. This is particularly important when investigating the biology of tissues with diverse cell types. Object-based colocalization analysis (OBCA) tools can employ automatic approaches, which are sensitive to errors in cell segmentation, or manual approaches, which can be impractical and tedious. Here, we present a novel set of tools for OBCA using a semi-automatic approach, consisting of two ImageJ plugins, a Microsoft Excel macro, and a MATLAB script. One ImageJ plugin enables customizable processing of multichannel 3D images for enhanced visualization of features relevant to OBCA, and another enables semi-automatic colocalization quantification. The Excel macro and the MATLAB script enable data organization and 3D visualization of object data across image series. The tools are well suited for experiments involving complex and large image data sets, and can be used in combination or as individual components, allowing flexible, efficient and accurate OBCA. Here we demonstrate their utility in immunohistochemical analyses of the developing central nervous system, which is characterized by complexity in the number and distribution of cell types, and by high cell packing densities, which can both create challenging situations for OBCA.

A tensor-based nonlocal total variation model for multi-channel image recovery

Abstract In this paper, a new nonlocal total variation (NLTV) regularizer is proposed for solving the inverse problems in multi-channel image processing. Different from the existing nonlocal total variation regularizers that rely on the graph gradient, the proposed nonlocal total variation involves the standard image gradient and simultaneously exploits three important properties inherent in multi-channel images through a tensor nuclear norm, hence we call this proposed functional as tensor-based nonlocal total variation (TenNLTV). In specific, these three properties are the local structural image regularity, the nonlocal image self-similarity, and the image channel correlation, respectively. By fully utilizing these three properties, TenNLTV can provide a more robust measure of image variation. Then, based on the proposed regularizer TenNLTV, a novel regularization model for inverse imaging problems is presented. Moreover, an effective algorithm is designed for the proposed model, and a closed-form solution is derived for a two-order complex eigen system in our algorithm. Extensive experimental results on several inverse imaging problems demonstrate that the proposed regularizer is systematically superior over other competing local and nonlocal regularization approaches, both quantitatively and visually.

Algebraic models and methods of computer image processing. Part 1. Multiplet models of multichannel images

We present a new theoretical framework for multichannel image processing using commutative hypercomplex algebras. Hypercomplex algebras generalize the algebras of complex numbers. The main goal of the work is to show that hypercomplex algebras can be used to solve problems of multichannel (color, multicolor, and hyperspectral) image processing in a natural and effective manner. In this work, we suppose that the animal brain operates with hypercomplex numbers when processing multichannel retinal images. In our approach, each multichannel pixel is considered not as an K–D vector, but as an K–D hypercomplex number, where K is the number of different optical channels. The aim of this part is to present algebraic models of subjective perceptual color, multicolor and multichannel spaces.

Supervised multichannel image classification algorithm using hierarchical histogram representation

Multichannel image processing, in particular, supervised classification, requires designing novel time effective algorithms, because in the most of the cases slightly dimension increase leads to significant processing time growth. In this paper we describe our supervised multichannel image classification algorithm based on a hierarchical representation of multivariate histograms. The algorithm estimates the joint sample set distribution, the particular distributions of each class and the decision rule by means of specific data structure called histogram-tree. Proposed algorithm provides faster learning and classification of multidimensional input data. The experimental evaluation of the algorithm has been conducted for the hyperspectral remote sensing images. The results demonstrate that proposed algorithm is faster than the commonly used C4.5 classifier.

A Class of Generalized Laplacians on Vector Bundles Devoted to Multi-Channel Image Processing

In the context of fibre bundles theory, there exist some differential operators of order 2, called generalized Laplacians, acting on sections of vector bundles over Riemannian manifolds, and generalizing the Laplace-Beltrami operator. Such operators are determined by covariant derivatives on vector bundles. In this paper, we construct a class of generalized Laplacians, devoted to multi-channel image processing, from the construction of optimal covariant derivatives. The key idea is to consider an image as a section of an associate bundle, that is a vector bundle related to a principal bundle through a group representation. In this context, covariant derivatives are determined by connection 1-forms on principal bundles. We construct optimal connection 1-forms by the minimization of a variational problem on principal bundles. From the heat equations of the generalized Laplacians induced by the corresponding optimal covariant derivatives, we obtain diffusions whose behaviors depend of the choice of the group representation. We provide experiments on color images.

Fidelity-Beltrami-Sparsity Model for Inverse Problems in Multichannel Image Processing

In multichannel image processing, many tasks, such as image denoising, image deblurring, and image inpainting, can be included in the fields of inverse problems and high-dimensional data processing. To get better performance, there are two key points that should be considered seriously. On the one hand, most of these inverse problems are ill-posed, and the prior constraints are necessary to transform them into well-posed problems by reducing the solution space. On the other hand, the information of the coupling among different channels is important in multichannel image processing, where each channel of the image is usually processed separately. In this paper, we propose the fidelity-Beltrami-sparsity (FBS) model for the inverse problems in multichannel image processing, which can simultaneously exploit two important priors, i.e., the smoothness prior and the sparsity prior. As we know, the smoothness prior and the sparsity prior have been widely validated for an image, respectively, so inverse problems w...

Automation of multichannel image processing

Automation of multichannel image processing

A methodology for time-frequency image processing applied to the classification of non-stationary multichannel signals using instantaneous frequency descriptors with application to newborn EEG signals

Abstract This article presents a general methodology for processing non-stationary signals for the purpose of classification and localization. The methodology combines methods adapted from three complementary areas: time-frequency signal analysis, multichannel signal analysis and image processing. The latter three combine in a new methodology referred to as multichannel time-frequency image processing which is applied to the problem of classifying electroencephalogram (EEG) abnormalities in both adults and newborns. A combination of signal related features and image related features are used by merging key instantaneous frequency descriptors which characterize the signal non-stationarities. The results obtained show that, firstly, the features based on time-frequency image processing techniques such as image segmentation, improve the performance of EEG abnormalities detection in the classification systems based on multi-SVM and neural network classifiers. Secondly, these discriminating features are able to better detect the correlation between newborn EEG signals in a multichannel-based newborn EEG seizure detection for the purpose of localizing EEG abnormalities on the scalp.

Multichannel image processing by using the Rank M-type L-filter

In this paper, we introduce the Vector Rank M-type L (VRML)-filter to remove impulsive noise from color images and video sequences. The proposed filter uses the Median M-type (MM) and Ansari–Bradley–Siegel–Tukey M-type (AM) estimators into L-filter to provide robustness to proposed filtering scheme. We also introduce the use of impulsive noise detectors to improve the properties of noise suppression and detail preservation in the proposed filtering scheme in the case of low and high densities of impulsive noise. Simulation results indicate that the proposed filter consistently outperforms other color image filters by balancing the trade-off between noise suppression, detail preservation, and color retention.

Design of Interchannel MRF Model for Probabilistic Multichannel Image Processing

In this paper, we present a novel framework that exploits an informative reference channel in the processing of another channel. We formulate the problem as a maximum a posteriori estimation problem considering a reference channel and develop a probabilistic model encoding the interchannel correlations based on Markov random fields. Interestingly, the proposed formulation results in an image-specific and region-specific linear filter for each site. The strength of filter response can also be controlled in order to transfer the structural information of a channel to the others. Experimental results on satellite image fusion and chrominance image interpolation with denoising show that our method provides improved subjective and objective performance compared with conventional approaches.

Active Uptake of Dendritic Cell-Derived Exovesicles by Epithelial Cells Induces the Release of Inflammatory Mediators through a TNF-α-Mediated Pathway

Dendritic cells (DCs) can release hundreds of membrane vesicles, called exovesicles, which are able to activate resting DCs and distribute antigen. Here, we examined the role of mature DC-derived exovesicles in innate and adaptive immunity, in particular their capacity to activate epithelial cells. Our analysis of exovesicle contents showed that exovesicles contain major histocompatibility complex-II, CD40, and CD83 molecules in addition to tumor necrosis factor (TNF) receptors, TNFRI and TNFRII, and are important carriers of TNF-alpha. These exovesicles are rapidly internalized by epithelial cells, inducing the release of cytokines and chemokines, but do not transfer an alloantigen-presenting capacity to epithelial cells. Part of this activation appears to involve the TNF-alpha-mediated pathway, highlighting the key role of DC-derived exovesicles, not only in adaptive immunity, but also in innate immunity by triggering innate immune responses and activating neighboring epithelial cells to release cytokines and chemokines, thereby amplifying the magnitude of the innate immune response.

Exovesicles from Human Activated Dendritic Cells Fuse with Resting Dendritic Cells, Allowing Them to Present Alloantigens

Dendritic cells (DCs) can release microvesicles, but the latter's numbers, size, and fate are unclear. Fluorescently labeled DCs were visualized by laser-scanning microscopy. Using a Surpass algorithm, we were able to identify and quantify per cell several hundred microvesicles released from the surface of stimulated DCs. We show that most of these microvesicles are not of endocytic origin but result from budding of the plasma membrane, hence their name, exovesicle. Using a double vital staining, we show that exovesicles isolated from activated DCs can fuse with the membrane of resting DCs, thereby allowing them to present alloantigens to lymphocytes. We concluded that, within a few hours from their release, exovesicles may amplify local or distant adaptive immunological response.

Iterative area filtering of multichannel images

Owing to the absence of total ordering between vectors of more than one dimension, most morphological transformations are not directly applicable to multichannel images. In this paper, we show that area based operators can be extended to the processing of multichannel images by using the quasi-flat zones and seeded region growing paradigms. The method is iterative in the sense that it starts with the smallest non-unitary quasi-flat zones until an automatically derived contrast threshold value is reached. Pseudo-code is given for all algorithms. We illustrate their usefulness for the simplification of complex natural images such as those occurring in satellite images of the earth.

Real-Time Color Image Processing Using Order Statistics Filters

The Vector Rank M-type K-Nearest Neighbour (VRMKNN) filter to remove impulsive noise from color images and video color sequences is presented. This filter utilizes multichannel image processing by using the vector approach and the Rank M-Type K-Nearest Neighbour (RMKNN) algorithm. Simulation results indicate that the proposed filter consistently outperforms other color image filters by balancing the tradeoff between noise suppression and detail preservation. The implementation of the filter was realized on the DSP TMS320C6711 to demonstrate that the proposed filter potentially could provide a real-time solution to quality video transmission.

Adaptive fuzzy order statistics-rational hybrid filters for color image processing

In this paper, multichannel image processing using an adaptive approach is studied. The proposed approach is simpler and more appropriate than the traditional approaches that have been addressed by means of groupwise vector ordering information. These adaptive techniques are formed by a two-layer filter based on rational functions using fuzzy transformations of either the Euclidean or angular distances among the different vectors to adapt to local data in the color image. The output is the result of a vector rational operation taking into account three fuzzy sub-function outputs. Extensive simulation results illustrate that the new adaptive fuzzy filters are computationally attractive and achieve noise attenuation, chromaticity retention, and edges and details preservation.

Geodesic Paths Approach to Color Image Enhancement

New filter class for multichannel image processing is introduced and analyzed. The new technique of image enhancement is capable of reducing impulsive and Gaussian noise and it significantly outperforms the standard methods of noise reduction. In the paper a smoothing operator, based on a random walk model and a fuzzy similarity measure between pixels connected by a digital geodesic path is introduced. The efficiency of the proposed method was tested on the standard color images using the objective image quality measures. Obtained results show that the new method not only outperforms standard noise reduction algorithms, but has some interesting features useful for segmentation of noisy color images.

On the reduction of impulsive noise in multichannel image processing

A new approach to the problem of impulsive-noise reduction for color images is introduced. The major advantage of the technique is that it filters out the noise component while adapting itself to the local image structures. In this way the algorithm is able to eliminate strong impulsive noise while preserving edges and fine image details. As the algorithm is a fuzzy modification of the commonly used vector median operator, it is very fast and easy to implement. Our results show that the proposed method outperforms all standard algorithms for the reduction of impulsive noise in color images. © 2001 Society of Photo-Optical Instrumenta- tion Engineers. (DOI: 10.1117/1.1367347)