Multichannel Image Research Articles

Satellite Image Restoration via an Adaptive QWNNM Model

Due to channel noise and random atmospheric turbulence, retrieved satellite images are always distorted and degraded and so require further restoration before use in various applications. The latest quaternion-based weighted nuclear norm minimization (QWNNM) model, which utilizes the idea of low-rank matrix approximation and the quaternion representation of multi-channel satellite images, can achieve image restoration and enhancement. However, the QWNNM model ignores the impact of noise on similarity measurement, lacks the utilization of residual image information, and fixes the number of iterations. In order to address these drawbacks, we propose three adaptive strategies: adaptive noise-resilient block matching, adaptive feedback of residual image, and adaptive iteration stopping criterion in a new adaptive QWNNM model. Both simulation experiments with known noise/blurring and real environment experiments with unknown noise/blurring demonstrated that the effectiveness of adaptive QWNNM models outperformed the original QWNNM model and other state-of-the-art satellite image restoration models in very different technique approaches.

A broadband hyperspectral image sensor with high spatio-temporal resolution

Hyperspectral imaging provides high-dimensional spatial–temporal–spectral information showing intrinsic matter characteristics1–5. Here we report an on-chip computational hyperspectral imaging framework with high spatial and temporal resolution. By integrating different broadband modulation materials on the image sensor chip, the target spectral information is non-uniformly and intrinsically coupled to each pixel with high light throughput. Using intelligent reconstruction algorithms, multi-channel images can be recovered from each frame, realizing real-time hyperspectral imaging. Following this framework, we fabricated a broadband visible–near-infrared (400–1,700 nm) hyperspectral image sensor using photolithography, with an average light throughput of 74.8% and 96 wavelength channels. The demonstrated resolution is 1,024 × 1,024 pixels at 124 fps. We demonstrated its wide applications, including chlorophyll and sugar quantification for intelligent agriculture, blood oxygen and water quality monitoring for human health, textile classification and apple bruise detection for industrial automation, and remote lunar detection for astronomy. The integrated hyperspectral image sensor weighs only tens of grams and can be assembled on various resource-limited platforms or equipped with off-the-shelf optical systems. The technique transforms the challenge of high-dimensional imaging from a high-cost manufacturing and cumbersome system to one that is solvable through on-chip compression and agile computation.

Airborne Multi-Channel Forward-Looking Radar Super-Resolution Imaging Using Improved Fast Iterative Interpolated Beamforming Algorithm

Radar forward-looking imaging is critical in many civil and military fields, such as aircraft landing, autonomous driving, and geological exploration. Although the super-resolution forward-looking imaging algorithm based on spectral estimation has the potential to discriminate multiple targets within the same beam, the estimation of the angle and magnitude of the targets are not accurate due to the influence of sidelobe leakage. This paper proposes a multi-channel super-resolution forward-looking imaging algorithm based on the improved Fast Iterative Interpolated Beamforming (FIIB) algorithm to solve the problem. First, the number of targets and the coarse estimates of angle and magnitude are obtained from the iterative adaptive approach (IAA). Then, the accurate estimates of angle and magnitude are achieved by the strategy of iterative interpolation and leakage subtraction in FIIB. Finally, a high-resolution forward-looking image is obtained through non-coherent accumulation. The simulation results of point targets and scenes show that the proposed algorithm can distinguish multiple targets in the same beam, effectively improve the azimuthal resolution of forward-looking imaging, and attain the accurate reconstruction of point targets and the contour reconstruction of extended targets.

Learned Tensor Neural Network Texture Prior for Photon-Counting CT Reconstruction.

Photon-counting computed tomography (PCCT) reconstructs multiple energy-channel images to describe the same object, where there exists a strong correlation among different channel images. In addition, reconstruction of each channel image suffers photon count starving problem. To make full use of the correlation among different channel images to suppress the data noise and enhance the texture details in reconstructing each channel image, this paper proposes a tensor neural network (TNN) architecture to learn a multi-channel texture prior for PCCT reconstruction. Specifically, we first learn a spatial texture prior in each individual channel image by modeling the relationship between the center pixels and its corresponding neighbor pixels using a neural network. Then, we merge the single channel spatial texture prior into multi-channel neural network to learn the spectral local correlation information among different channel images. Since our proposed TNN is trained on a series of unpaired small spatial-spectral cubes which are extracted from one single reference multi-channel image, the local correlation in the spatial-spectral cubes is considered by TNN. To boost the TNN performance, a low-rank representation is also employed to consider the global correlation among different channel images. Finally, we integrate the learned TNN and the low-rank representation as priors into Bayesian reconstruction framework. To evaluate the performance of the proposed method, four references are considered. One is simulated images from ultra-high-resolution CT. One is spectral images from dual-energy CT. The other two are animal tissue and preclinical mouse images from a custom-made PCCT systems. Our TNN prior Bayesian reconstruction demonstrated better performance than other state-of-the-art competing algorithms, in terms of not only preserving texture feature but also suppressing image noise in each channel image.

Photosystems functioning, the content of chemical elements, flavonoids and brassinosteroids in leaves of chimera Cornus controversa

he formation of chimera is a well-known phenomenon in variegated ornamental and wild plants, but the biochemical peculiarities of this phenomenon are not well studied. The purpose of this work was to conduct a comparative study of the photosynthetic apparatus functioning, the content of macro-, microelements, flavonoids and brassinosteroids in the variegated and green leaves of the periclinal chimera Cornus controversa plant grown in the collection of M. M. Gryshko National Botanical Garden of the NAS of Ukraine. Sampling was made during the period from June to October 2022. The content of photosynthetic pigments and brassinosteroids was measured spectrophotometrically, autofluorescence of leaf tissues was studied using an inverted microscope with a multi-channel fluorescence imaging system, Normalized difference vegetation index (NDVI) – using GreenSeeker devices and fluorimeter, the level of macro- and microelements – using plasma emission spectrometer. Phenolic secondary metabolites were separated by HPTLC. To assess the protective capacity of flavonoids, a bioassay on the germination of radish seeds Raphanus in the presence of AlCl3 was used. It was shown that green leaves of C. controversa chimera, compared to variegated ones, had a twice as high NDVI index, maintained photosynthetic function longer in autumn, and had higher content of macronutrients (Mg, Ca and S) but lower concentration of toxic metals (Al, Cu, Fe, Zn, Ti). Instead, variegated leaves accumulated more brassinosteroids and phenolic metabolites, which were found not only to reduce the toxic effect of aluminum but also to have a growth-stimulating effect on the roots of radish seedlings. Such specialization of green and variegated leaves may contribute to better plant chimeras adaptation to environmental pollution. Keywords: brassinosteroids, Cornus controversa, macroelements, microelements, NDVI index, periclinal chimera, phenolic metabolites, photosynthetic pigments, tissue autofluorescence

Rapid wavelength interval selection technology for multi-channel intensity-interrogation surface plasmon resonance imaging sensing

Intensity-interrogation surface plasmon resonance (I-SPR) provides the fastest imaging speed among various SPR techniques, including wavelength, phase, and angle-based detection. It also integrates easily with other devices and has substantial practical application potential. However, I-SPR's reliance on direct detection of reflected light intensity renders it sensitive to light source fluctuations and detector dark noise, and its dynamic range is relatively limited. To address these challenges, we have developed a multi-channel I-SPR sensing platform featuring rapid wavelength interval selection technology. This system optimizes the sensing wavelength according to the refractive indices of different samples, mitigating issues related to inconsistent SPR signals from various biomolecules. Our improved I-SPR technology enables high-throughput biomolecular detection with a refractive index range size of 2.035 × 10−2 for dynamic monitoring, reaching a leading role among the existing I-SPR technologies. This represents the widest linear dynamic range for I-SPR to date. Experimental results on protein binding kinetic constants KD are consistent with those obtained from commercially available instruments. We believe that this study is expected to accelerate the development of SPR technology toward broader biological applications.

Analysis of leakage artifacts and their impact on convergence of algebraic reconstruction in multi-contrast magnetic particle imaging

Objective.Magnetic particle imaging (MPI) is a tracer-based medical imaging modality with great potential due to its high sensitivity, high spatiotemporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem, which the use of regularization methods can address. Multi-contrast MPI reconstructs the signal from different tracer materials or environments separately, resulting in multi-channel images that enable quantification of, for example, temperature or viscosity. Single- and multi-contrast MPI reconstructions produce different kinds of artifacts. The objective of this work is threefold: first, to present the concept of multi-contrast specific MPI channel leakage artifacts; second, to ascertain the source of these leakage artifacts; and third, to introduce a method for their reduction.Approach.A definition for leakage artifacts is established, and a quantification method is proposed. A comprehensive analysis is conducted to establish a connection between the properties of the multi-contrast MPI system matrix and the leakage artifacts. Moreover, a two-step measurement and reconstruction method is introduced to reduce channel leakage artifacts between multi-contrast MPI channels.Main results.The severity of these artifacts correlates with the system matrix shape and condition number and depends on the similarity of the corresponding frequency components. Using the proposed two-step method on both semi-simulated and measured data a significant leakage reduction and speed up the convergence of the multi-contrast MPI reconstruction was observed.Significance.The multi-contrast system matrix analysis we conducted is essential for understanding the source of the channel leakage artifacts and finding methods to reduce them. Our proposed two-step method is expected to improve the potential for real-time multi-contrast MPI applications.

Blind CT Image Quality Assessment Using DDPM-Derived Content and Transformer-Based Evaluator.

Lowering radiation dose per view and utilizing sparse views per scan are two common CT scan modes, albeit often leading to distorted images characterized by noise and streak artifacts. Blind image quality assessment (BIQA) strives to evaluate perceptual quality in alignment with what radiologists perceive, which plays an important role in advancing low-dose CT reconstruction techniques. An intriguing direction involves developing BIQA methods that mimic the operational characteristic of the human visual system (HVS). The internal generative mechanism (IGM) theory reveals that the HVS actively deduces primary content to enhance comprehension. In this study, we introduce an innovative BIQA metric that emulates the active inference process of IGM. Initially, an active inference module, implemented as a denoising diffusion probabilistic model (DDPM), is constructed to anticipate the primary content. Then, the dissimilarity map is derived by assessing the interrelation between the distorted image and its primary content. Subsequently, the distorted image and dissimilarity map are combined into a multi-channel image, which is inputted into a transformer-based image quality evaluator. By leveraging the DDPM-derived primary content, our approach achieves competitive performance on a low-dose CT dataset.

Pre-Trained Transformer-Based Parallel Multi-Channel Adaptive Image Sequence Interpolation Network

Pre-Trained Transformer-Based Parallel Multi-Channel Adaptive Image Sequence Interpolation Network

SCIP: A scalable, reproducible and open-source pipeline for morphological profiling of image cytometry and microscopy data.

Imaging flow cytometry (IFC) provides single-cell imaging data at a high acquisition rate. It is increasingly used in image-based profiling experiments consisting of hundreds of thousands of multi-channel images of cells. Currently available software solutions for processing microscopy data can provide good results in downstream analysis, but are limited in efficiency and scalability, and often ill-adapted to IFC data. In this work, we propose Scalable Cytometry Image Processing (SCIP), a Python software that efficiently processes images from IFC and standard microscopy datasets. We also propose a file format for efficiently storing IFC data. We showcase our contributions on two large-scale microscopy and one IFC datasets, all of which are publicly available. Our results show that SCIP can extract the same kind of information as other tools, in a much shorter time and in a more scalable manner.

CWC-MP-MC Image-based breast tumor classification using an optimized Vision Transformer (ViT)

This study introduces a novel approach to improve breast tumor classification by integrating advanced image processing techniques and a state-of-the-art Vision Transformer (ViT) model. Our methodology involves transforming B-mode ultrasound images into Cross-Correlated Weighted Contourlet Multi-Parametric Multi-channel (CWC-MP-MC) images. This process includes applying Nakagami, Normal Inverse Gaussian (NIG), and Rician Inverse Gaussian (RiIG) statistical modeling to generate three distinct channels representing different statistical properties of the ultrasound data. These ultrasound data undergo a multi-resolution transform domain like contourlet transform and are weighted by cross-correlation to produce the CWC-MP-MC image. This composite image encapsulates comprehensive information about breast tissue characteristics, offering a robust representation for tumor classification. For classification, we utilize a optimized Vision Transformer (ViT) architecture specially designed to be lightweight, fine-tuned, and suitable for operating in a low-configuration GPU environment. Our experiments, conducted on three publicly available datasets (Mendeley, UDIAT, and BUSI), demonstrate that our proposed methodology achieves accuracy, sensitivity, specificity, NPV, PPV, and F1 scores exceeding 98% when employing CWC-MP-MC images.

A HRWS SAR Motion Compensation Method with Multichannel Phase Correction

The multichannel synthetic aperture radar (SAR) possesses the capability to acquire high-resolution, wide-swath SAR imagery, which has great potential for application. However, similar to traditional single-channel SAR systems, it suffers from imaging quality degradation due to motion errors. Many motion compensation algorithms have been used to improve the quality of single-channel SAR images, while fewer studies have been conducted on multichannel SAR motion compensation methods. The sub-image motion compensation method utilizes the single channel motion errors to perform multichannel motion errors compensation, considering that multiple channels have the same phase errors. To improve the quality of multichannel SAR imaging when multiple channel motion errors are inconsistent, this paper proposes a motion compensation method with multichannel phase correction for HRWS SAR. First, the method derives the phase errors estimation model via maximum sharpness to simultaneously estimate multichannel phase. Then, it compensates for the motion errors of all channels during backprojection imaging. The inconsistent motion errors of multiple channels can be compensated by estimating the phase errors of all channels, improving the image quality. The channel phase errors can be corrected while compensating for the motion errors. Moreover, the experimental results of point targets and complex scenes validate the effectiveness of the proposed method.

Application of artificial neural network for description of the Earth remote sensing data to identify natural habitats used by European elk, Alces alces (Linnaeus, 1758), in autumn-winter period 2023

The paper presents the results of using neural networks of pixel-by-pixel image classification to decipher multichannel space images obtained by the Landsat 8 satellite. Natural habitats were identified based on differences in phytocenotic characteristics of land areas. The level of use of areas by elk was determined by the occurrence of elk presence indicators, which were the density of winter excrement piles (in spring) and the abundance of winged forms of the monoxenous ectoparasite of elk, Lipoptena cervi (Linnaeus, 1758), as measured by the number of individuals attacking the census taker (in the summer-autumn period). The material that forms the basis for deciphering was obtained by the route census method in different areas of the Leningrad Province. High values of the measured parameters were recorded in reforestation areas, in wet pine and birch forests, in aspen forests, on the edges of raised bogs and in overgrown fields – typical elk winter feeding habitats. Based on the data on the maximum values of the measured parameters, a positive correlation was established for the first time between the abundance of winged forms of L. cervi and the physiological activity of the host (the density of the piles of “winter” excrement). The paper also provides a valid reference to the material of schematic maps developed for a part of the territory of the Leningrad Province and Saint Petersburg. The scheme of the use of areas by elk is compiled on the basis of the relative index calculated from the measured parameters of the survey. Field observation areas and territories close to them were most reliably identified (90–95% accuracy). For areas not covered by field observations, the paper provides comments on the main inaccuracies of the schemes and the reasons for the identified discrepancies, based on an analysis of literary sources devoted to similar research topics. The results of the study indicate that the frequency of attacks of winged forms of L. cervi can be used as an additional criterion for assessing the stationary preferences of elk in the winter period using information on the period of active flight, and the decoding method mentioned in the paper can be used in limited areas of specific territories, including reserves, areas of hunting farms and district forestries.

Retraction Note: Application of multi-channel optical imaging automatic classification in outdoor scene environment design

Retraction Note: Application of multi-channel optical imaging automatic classification in outdoor scene environment design

An enhanced digital twin-driven fault detection and isolation method based on sensor series imaging mechanism for gas turbine engine

Condition based maintenance plays a crucial role in ensuring the safety and reliability of gas turbine engine. Specifically, fault detection and isolation (FDI) can improve efficiency and reduce costs during maintenance. The traditional FDI methods, which developed only using physical engine data, lack a comprehensive consideration of high accuracy and strong applicability across different engine individuals. In this paper, an enhanced digital twin-driven FDI method is proposed, which combines a digital engine, sensor series imaging mechanism and a convolutional neural network. The digital engine is constructed to mirror the operational status of the physical engine. The output residuals between the physical and digital engines are transformed into multi-channel images to train a convolutional neural network for FDI of the physical engine. The network is then fine-tuned using fault-free data from the target engine to further strengthen diagnostic performance across different engine individuals. The effectiveness of the digital engine is verified using actual ground test data, and the performance of the proposed method is demonstrated through comparative simulations. The network, trained on operational data from Engine A and fine-tuned with fault-free data from other engines, exhibits the highest diagnostic accuracy and fault isolation rate. Compared with the physical engine data-based method, the accuracy is increased from 68.25% to 97.95%, the fault isolation rate is improved from 85.91% to 99.26%, and the false alarm rate is decreased from 4.41% to 0.34%. The results show that the proposed method has better FDI capabilities across different engine individuals.

3D mineral prospectivity modeling at the Axi epithermal gold deposit, NW China by using a feature adaptive fusion strategy

Mineralization distribution is always intricately affected by multiple ore-controlling geological units that play different roles in a mineral system (e.g., driver, trap, and throttle). How to effectively balance and integrate ore-controlling features from various 3D geological models during 3D mineral prospectivity modeling (MPM) is still a challenging task. In this paper, we introduce a novel approach, the feature adaptive fusion convolutional neural networks (CNN), which is designed to learn multiple 3D geological models with ore-controlling functions. The method is validated in the Axi epithermal gold deposit, northwestern China that mineralization distribution is jointly controlled by fault, volcanic phase, and phyllic alteration. The geology units are firstly constructed by explicit-implicit modeling and their ore-controlling features are subsequently described by high-frequency Laplace-Beltrami eigenfunctions and reassembled into multi-channel images as input to CNN. To learn the differences in ore-controlling effects among various geological units, we designed a fully connected layer to achieve adaptive quantification and weighted integration of the ore-controlling features by automatically optimizing weight allocation parameters and bias vectors using the neural network intelligence. Comparison results between the proposed method and other prospectivity methods suggest that the feature adaptive fusion CNN produces more reliable predictions, characterized by: (1) high consistency with known mineralization, (2) the highest AUC value and success rate, and (3) accurate prediction of deep voxels explored by drilling. Therefore, the proposed method effectively integrates the ore-controlling effects of multiple geological units and is suitable for complex scenarios of 3D MPM. Utilizing the prospectivity results generated by our method, we identified five potential mineralization in the Axi gold deposit, laying a robust foundation for future gold exploration.

A Novel Truncated Norm Regularization Method for Multi-Channel Color Image Denoising

A Novel Truncated Norm Regularization Method for Multi-Channel Color Image Denoising

An integrated multichannel system for air-coupled CMUT array imaging and guided wave detection applications

Abstract Capacitive micromachined ultrasonic transducers (CMUTs) made by MEMS process have become a new choice for air-coupled ultrasonic transducers due to their high sensitivity, low acoustic impedance and easy arraying. At present, in the air coupling application of CMUT transducer, due to the weak signal strength of single element, the CMUT array usually works in a whole piece, limiting the advantage of the array. Therefore, a multi-channel air-coupled CMUT imaging system with high-voltage DC bias and low-noise high-gain current signal amplification is designed to realize the electronic scanning of our CMUT arrays. The imaging system has 16 transmit and receive channels, and each channel consists of pulse transmitting circuit, AC/DC coupling circuit, impedance matching circuit, low-noise transimpedance amplification circuit, controllable gain amplification circuit and AD circuit. The imaging chain and element control method of our CMUT array were developed to realize the electronic scanning imaging of air-coupled ultrasound. The feasibility of the system for non-destructive testing was also verified via the Lamb wave detection in a metal plate.…

Motor fault diagnosis based on multisensor-driven visual information fusion

To need of accurate motor fault diagnosis in industrial system, we propose a fault diagnosis framework that utilizes motor current and electromagnetic signals, combining them with a self-attention-enhanced capsule network for enhanced signal analysis and accuracy. Firstly, the original signal extracted by multiple sensors is constructed into a symmetric point mode (SDP) image, and the visual fault information of different sensors and fusion signals of different motion health states are obtained by the proposed multi-channel image fusion method. Then, the capsule network, combined with self-attention, extracts spatial features from the high-dimensional tensor of the multi-channel fused image for adaptive recognition and extraction. Subsequently, advanced feature vector information is obtained through softmax for diagnosis. Diagnosis results of several datasets indicate that the developed diagnosis framework with compressed image information can availably identify 8 kinds of motor fault states under various loads, and the fault diagnosis rate is as high as 99.95 %, it is helpful for low cost and high-speed diagnosis of motors. In addition, by learning multiple sensor signals in the same state, it obtains stronger robustness and effectiveness than a single signal model.

Robust hyperspectral reconstruction via a multi-channel clustering compressive sensing approach

Wavelength-coded spectral imaging represents a fusion of spectral imaging and compressed sensing, offering advantages such as reduced storage requirements and straightforward miniaturization. This approach employs optical filters for hyperspectral reconstruction. However, issues like insufficient energy and excessive noise arise in low-light detection, leading to a notable degradation of image reconstruction quality. This paper presents a robust segmented reconstruction algorithm named MCC-WSCI (Multi-Channel Clustering-based Wavelength-Coded Spectral Imaging), designed to enhance the method's tolerance to noise. This algorithm can accurately classify and process compressed images obtained by wavelength-coded spectral imaging systems, thus improving reconstruction quality significantly. Numerical simulations and experiments in low-light scenarios are carried out to verify the proposed method. Results show that the MCC-WSCI method is robust to different noise and sampling rates and outperforms other state-of-the-art compressed sensing reconstruction methods in terms of reconstructed spatial resolution and spectral resolution. The proposed method provides effective experimental robustness to wavelength-coded spectral imaging with a natural algorithmic extension, paving the way for its application in remote sensing.