Multiscale Decomposition Method Research Articles

Infrared and visible image fusion based on hybrid multi-scale decomposition and adaptive contrast enhancement

Effectively fusing infrared and visible images enhances the visibility of infrared target information while capturing visual details. Balancing the brightness and contrast of the fusion image adequately has posed a significant challenge. Moreover, preserving detailed information in fusion images has been problematic. To address these issues, this paper proposes a fusion algorithm based on multi-scale decomposition and adaptive contrast enhancement. Initially, we present a hybrid multi-scale decomposition method aimed at extracting valuable information comprehensively from the source image. Subsequently, we advance an adaptive base layer optimization approach to regulate the brightness and contrast of the resultant fusion image. Lastly, we design a weight mapping rule grounded in saliency detection to integrate small-scale layers, thereby conserving the edge structure within the fusion outcome. Both qualitative and quantitative experimental results affirm the superiority of the proposed method over 11 state-of-the-art image fusion methods. Our method excels in preserving more texture and achieving higher contrast, which proves advantageous for monitoring tasks.

On the benefits of a multiscale domain decomposition method to model-order reduction for frictional contact problems

In this paper, the efficiency of a multiscale strategy based on a domain decomposition method (DDM) for model-order reduction of time-dependent frictional contact problems is presented. The proposed strategy relies on the LArge Time INcrement (LATIN) nonlinear solver combined with model reduction based on the Proper Generalized Decomposition (PGD). The LATIN presents a robust treatment of contact conditions, sharing similarities with augmented Lagrangian approaches, and naturally leads to a mixed DDM. In addition, the global space–time formulation of the method allows PGD-based model reduction to be used during computations, creating and enriching on-the-fly reduced bases per substructure to better track sliding fronts and propagative phenomena. The introduction of a multiscale strategy in the LATIN framework is consistent with the physics of contact problems, in which phenomena with different wavelengths interact: local solutions at contact interfaces presents high gradient effects with a short wavelength compared to the characteristic length of the structure. By taking advantage of this, the coarse scale problem of the strategy enables to capture efficiently the behavior of the problem at the structural level, focusing then on capturing the local contact variations at the contact interfaces. The most important features of the approach are shown comprehensively on a simple one-dimensional frictional contact problem. Then, its robustness and effectiveness are tested on a two-dimensional multibody frictional contact problem with more complex loadings. Guidelines are also given for choosing the parameters of the strategy, in particular those driving the construction of the reduced basis.

A multi-scale fuel cell degradation prediction method based on isometric convolution block and long short-term memory networks

Performance degradation prediction is considered an effective method to enhance the durability of Proton Exchange Membrane Fuel Cells (PEMFCs). However, accurate prediction under dynamic conditions remains a challenge. The MSD-ICB-LSTM model is proposed to improve the accuracy of the performance degradation prediction, which incorporates the information from both long-term and short-term modes within the PEMFC history operating data. Firstly, redundant variables are removed from the data, and the noise is eliminated using locally weighted scatterplot smoothing (LOWESS) to raise the quality of the input samples. Secondly, the Multi-Scale Decomposition (MSD) method is used to decompose the pre-processed data into long-term and short-term modes. Thirdly, a combined module containing encoding, internal decomposition and a double-layer Isometric Convolution Block (ICB) is used to deeply explore the nonlinear degradation characteristics in the short-term mode, while a Long-Short-Term Memory Network (LSTM) is utilized to estimate the long-term mode. The final result of prediction is obtained by combining the predictions of two modes. The experimental results show that the prediction accuracy of the proposed MSD-ICB-LSTM model is 99.74% in R2，and the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are 0.0034 and 0.77%, respectively. Thus, the method can efficiently explore the multi-scale features and enhance the accuracy of PEMFC performance degradation prediction.

MCADFusion: a novel multi-scale convolutional attention decomposition method for enhanced infrared and visible light image fusion

MCADFusion: a novel multi-scale convolutional attention decomposition method for enhanced infrared and visible light image fusion

Comparative Analysis of Pixel-Level Fusion Algorithms and a New High-Resolution Dataset for SAR and Optical Image Fusion

Synthetic aperture radar (SAR) and optical images often present different geometric structures and texture features for the same ground object. Through the fusion of SAR and optical images, it can effectively integrate their complementary information, thus better meeting the requirements of remote sensing applications, such as target recognition, classification, and change detection, so as to realize the collaborative utilization of multi-modal images. In order to select appropriate methods to achieve high-quality fusion of SAR and optical images, this paper conducts a systematic review of current pixel-level fusion algorithms for SAR and optical image fusion. Subsequently, eleven representative fusion methods, including component substitution methods (CS), multiscale decomposition methods (MSD), and model-based methods, are chosen for a comparative analysis. In the experiment, we produce a high-resolution SAR and optical image fusion dataset (named YYX-OPT-SAR) covering three different types of scenes, including urban, suburban, and mountain. This dataset and a publicly available medium-resolution dataset are used to evaluate these fusion methods based on three different kinds of evaluation criteria: visual evaluation, objective image quality metrics, and classification accuracy. In terms of the evaluation using image quality metrics, the experimental results show that MSD methods can effectively avoid the negative effects of SAR image shadows on the corresponding area of the fusion result compared with CS methods, while model-based methods exhibit relatively poor performance. Among all of the fusion methods involved in the comparison, the non-subsampled contourlet transform method (NSCT) presents the best fusion results. In the evaluation using image classification, most experimental results show that the overall classification accuracy after fusion is better than that before fusion. This indicates that optical-SAR fusion can improve land classification, with the gradient transfer fusion method (GTF) yielding the best classification results among all of these fusion methods.

Multiscale hierarchical decomposition methods for ill-posed problems

The multiscale hierarchical decomposition method (MHDM) was introduced in Tadmor et al (2004 Multiscale Model. Simul. 2 554–79; 2008 Commun. Math. Sci. 6 281–307) as an iterative method for total variation (TV) regularization, with the aim of recovering details at various scales from images corrupted by additive or multiplicative noise. Given its success beyond image restoration, we extend the MHDM iterates in order to solve larger classes of linear ill-posed problems in Banach spaces. Thus, we define the MHDM for more general convex or even nonconvex penalties, and provide convergence results for the data fidelity term. We also propose a flexible version of the method using adaptive convex functionals for regularization, and show an interesting multiscale decomposition of the data. This decomposition result is highlighted for the Bregman iteration method that can be expressed as an adaptive MHDM. Furthermore, we state necessary and sufficient conditions when the MHDM iteration agrees with the variational Tikhonov regularization, which is the case, for instance, for one-dimensional TV denoising. Finally, we investigate several particular instances and perform numerical experiments that point out the robust behavior of the MHDM.

Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects

Infrared and visible light image fusion combines infrared and visible light images by extracting the main information from each image and fusing it together to provide a more comprehensive image with more features from the two photos. Infrared and visible image fusion has gained popularity in recent years and is increasingly being employed in sectors such as target recognition and tracking, night vision, scene segmentation, and others. In order to provide a concise overview of infrared and visible picture fusion, this paper first explores its historical context before outlining current domestic and international research efforts. Then, conventional approaches for infrared and visible picture fusion, such as the multi-scale decomposition method and the sparse representation method, are thoroughly introduced. The advancement of deep learning in recent years has greatly aided the field of picture fusion. The outcomes of the fusion have a wide range of potential applications due to the neural networks’ strong feature extraction and reconstruction skills. As a result, this research also evaluates deep learning techniques. After that, some common objective evaluation indexes are provided, and the performance evaluation of infrared and visible image fusion is introduced. The common datasets in the areas of infrared and visible image fusion are also sorted out at the same time. Datasets play a significant role in the advancement of infrared and visible image fusion and are an essential component of infrared and visible image fusion testing. The application of infrared and visible image fusion in many domains is then simply studied with practical examples, particularly in developing fields, used to show its application. Finally, the prospect of the current infrared and visible image fusion field is presented, and the full text is summarized.

Remote Sensing Pansharpening with TV-H−1 Decomposition and PSO-Based Adaptive Weighting Method

In remote sensing, owing to existing sensors’ limitations and the tradeoff between signal-to-noise ratio (SNR) and instantaneous field of view (IFOV), it is difficult to obtain a single image with good spectral and spatial resolution. Pansharpening (PS) is the technique for sharpening multispectral (MS) images by extracting structural and edge information of panchromatic (PAN) image. Multiscale decomposition methods are used for decomposing image in sub-bands but are affected by ringing artifacts, therefore the resultant image seems to be blurred and misregistered. The proposed method overcomes this drawback by decomposing PAN and four band MS image into cartoon and texture components with total variation (TV) Hilbert[Formula: see text] model. The particle swarm optimization (PSO) algorithm is used for finding the optimum weight for fusing texture and cartoon details of PAN and MS images. The proposed method is practically validated on both full-scale and reduced-scale. Robustness of our proposed approach is tested on different geographical areas such as hilly, urban, and vegetation areas. From the visual analysis and qualitative parameters, the proposed method is proved effective compared with other traditional approaches.

Crustal Structure and Geothermal Mechanism of the Gonghe-Guide Basin Based on EIGEN-6C4 Satellite Gravity and Aeromagnetic Data

The Gonghe-Guide Basin is situated in the northeastern edge of the Tibetan Plateau, one of China’s main targets for geothermal research and exploitation. We present a new crustal structure of the entire basin by using EIGEN-6C4 satellite gravity and aeromagnetic data aimed to further recognize the geothermal mechanisms. The data are processed using the wavelet multi-scale decomposition method and the iterative compact depth from extreme points imaging method. The gravity inversion results reveal distinct low-density regions in the middle-upper crust at 15–35 km depth, which is most likely caused by partial melting. The magnetic inversion results show negative or no magnetism within a similar depth range. But at shallow depths of the same horizontal positions, there is high positive magnetism, which can be interpreted as granite. The results correspond well with the magnetotelluric and seismic results and can be an effective supplement. To confirm and validate this conclusion, a 2D geological model of a profile from a typical area is created to show the detailed tectonic. Based on the new crustal structure results, the suggested geothermal target is the low/negative density corresponding to low/negative magnetism located at deep depths and high/positive magnetism located at shallow depths. Then we established a geological conceptual model to illustrate this process. This indicates that tectonic movement is taking place in the deep part of the earth in the Gonghe-Guide Basin. The research on the geological structure and geothermal heat source mechanism in the Gonghe-Guide Basin can provide a primary reference for research on geothermal resources in other areas with similar geological structures.

Multiscale analyse of the relation between skid resistance and pavements surfaces texture evolution with polishing

It is known from the literature that skid resistance is closely related to road surface roughness. This paper aims to develop a multiscale texture decomposition method able to identify the relevant scales that affect pavement surfaces’ skid resistance variation during polishing. This method is based on continuous wavelets decomposition and. It is applied on seven pavement surfaces before and after polishing. The polishing is done in the laboratory using the Wehner & Schulze apparatus, which also serves for skid esistance measurements. At each step of polishing, 3D topographical maps of the test surface are realised with an optical sensor. On these maps, multiscale texture parameters are calculated after the multiscale decomposition. Analysis of these parameters shows their ability to represent the increase and the rate of increase in skid resistance in early age (increase in multiscale parameters for scale range 300–3000 µm) of pavement surfaces and the long-time skid resistance decrease (decrease in multiscale parameters for scale range 0–1000 µm.

Dynamic Range Compression of Thermograms for Assessment of Welded Joint Face Quality.

Temperature is one of the essential parameters in fusion welding. Typically, an uncooled infrared detector acquires 14-bit data, while a human observer can only distinguish about 128 levels of grey. For IR HDR (high dynamic range) images, one of the main goals of dynamic range compression is to enhance the visibility of low-contrast details. It is an important issue because the temperature span in the cross-section of a welded joint and its length are large. In the paper, global approaches for range compression are investigated, such as algorithms that include pixel transformations, histogram equalization ('he') and some of its variants. Additionally, multiscale decomposition methods were investigated. All results are obtained for the sequences of thermograms acquired during the TIG welding of plates made of Inconel 625 superalloy. The process was observed with an uncooled IR camera. The application of compression methods led to the generation of low-dynamic-range (LDR) IR images. The algorithms allowed the preservation of global contrast and enhancement of the visibility of hot details in dark and low-contrast areas. All IR representations of the welded samples were evaluated, and relationships between apparent temperature counted in the pixel-level value and weld-face geometry were revealed. Methods based on wavelet transforms were found to be the most suitable for this type of image; nevertheless, a relatively large local noise was generated.

Generalized multi-scale image decomposition for new tone manipulation

It is well known that the image multi-scale decomposition plays a foundational role in image processing and a variety of image multi-scale decomposition methods have been derived in various image processing tasks up till now. In many cases, the image decomposition needs to satisfy the conditions: edge-preserving or non-scale-hybridizing. Unfortunately, there has been no such multi-scale image decomposition approach satisfying both the two conditions simultaneously to our best knowledge. In this paper, one novel multi-scale image decomposition approach satisfying both the two conditions is proposed, which achieves different edge-preserving components under different scales. The proposed approach makes full use of well-designed adaptive mean envelope pixels to construct the segmentation envelopes to separate the different scales. Moreover, based on the multi-scale decomposition, an interesting multi-scale tone manipulation scheme and an effective new filtering algorithm for impulse noise are induced. Furthermore, numerical experiments compared with the existent methods are given to demonstrate the state-of-the-art performance of the proposed methods.

An Adaptive Multiscale Gaussian Co-Occurrence Filtering Decomposition Method for Multispectral and SAR Image Fusion

An Adaptive Multiscale Gaussian Co-Occurrence Filtering Decomposition Method for Multispectral and SAR Image Fusion

Infrared and Visible Image Fusion Based on Visual Saliency Map and Image Contrast Enhancement.

The purpose of infrared and visible image fusion is to generate images with prominent targets and rich information which provides the basis for target detection and recognition. Among the existing image fusion methods, the traditional method is easy to produce artifacts, and the information of the visible target and texture details are not fully preserved, especially for the image fusion under dark scenes and smoke conditions. Therefore, an infrared and visible image fusion method is proposed based on visual saliency image and image contrast enhancement processing. Aiming at the problem that low image contrast brings difficulty to fusion, an improved gamma correction and local mean method is used to enhance the input image contrast. To suppress artifacts that are prone to occur in the process of image fusion, a differential rolling guidance filter (DRGF) method is adopted to decompose the input image into the basic layer and the detail layer. Compared with the traditional multi-scale decomposition method, this method can retain specific edge information and reduce the occurrence of artifacts. In order to solve the problem that the salient object of the fused image is not prominent and the texture detail information is not fully preserved, the salient map extraction method is used to extract the infrared image salient map to guide the fusion image target weight, and on the other hand, it is used to control the fusion weight of the basic layer to improve the shortcomings of the traditional ‘average’ fusion method to weaken the contrast information. In addition, a method based on pixel intensity and gradient is proposed to fuse the detail layer and retain the edge and detail information to the greatest extent. Experimental results show that the proposed method is superior to other fusion algorithms in both subjective and objective aspects.

Infrared and visible image fusion for ship targets based on scale‐aware feature decomposition

Infrared (IR) and visible (VI) image fusion play an important role in improving the sea scene perception and ship target detection. Although there have been many studies on image fusion considering the sea scene characteristics, there has still been no adequate extraction method for detailed information of a ship target in a sea scene. To overcome this shortcoming, this paper proposes a fusion method based on a scale-aware feature decomposition, which can accurately extract features of a ship target at different scales. First, a hybrid feature decomposition method based on the scale-aware structure-preserving filter and Gaussian filter is designed. The proposed method separated source images into region, structure, and texture layers, and thus achieved a finer-scale division than traditional multiscale decomposition methods in the sea-clutter background. Then, further decomposition of the texture layer was performed by the shearlet transform to obtain the directional texture feature. According to the characteristics of each layer, different strategies were adopted for the fusion, and the weighting factor was used to obtain the final fusion result. Experimental results indicated that the proposed method could achieve better subjective and objective results than current state-of-the-art methods. This method introduces a scale-aware edge preserving filter to improve the ability of detail extraction and is suitable for image fusion of ship targets.

A novel carbon price combination forecasting approach based on multi-source information fusion and hybrid multi-scale decomposition

Accurate carbon price forecasting is essential to reduce carbon dioxide emissions and slow down global warming. However, a key issue in the carbon trading market is the diversity and uncertainty of external factors. Some studies began to focus on the impact of a single external factor, but few of them considered the application of multi-source information on carbon prices. In addition, the selection of the decomposition method is still controversial, making carbon price forecasting inefficient and unstable. Therefore, this paper proposes a carbon price forecasting method based on multi-source information fusion (MSIF) and hybrid multi-scale decomposition (HMSD). First, MSIF can provide complete, interactive, and timely information for raw carbon prices, including historical data, influencing factors (coal prices, oil prices), and unstructured data (Baidu index, social media sentiment). Second, HMSD is used to completely extract the internal features of multi-source information and avoid the problem of decomposition method selection. Third, due to the linear and nonlinear characteristics of carbon prices, a combination strategy based on Holt, ARIMA, SVR, BPNN, and LSTM can achieve satisfactory results. Finally, to evaluate the effectiveness of the proposed framework, seven types of comparative experiments (based on historical data, influencing factors, Baidu index, and sentiment analysis) are carried out. The results show that MSIF is superior to single-source information in improving carbon price forecasting performance. Furthermore, the HMSD is stronger than the single multi-scale decomposition method in information extraction. Therefore, the proposed hybrid framework is a state-of-the-art carbon price forecasting approach.

Nonlinear Dependence and Spillovers between Currency Markets and Global Economic Variables

The widespread integration and growing systemic dependence among currency, stock, and commodity markets render these markets often very vulnerable to shocks and at risk of collapse at the same time. As a result, these trends threaten the sustainability of the entire financial system. In this study, we aim to explore the spillovers and nonlinear dependencies between the seven major foreign exchange rates, crude oil and gold prices, a global stock price index, and oil and stock implied volatility indices as proxy variables for global risk factors by employing a directional spillover network approach. We also use a multi-scale decomposition method and nonlinear causality test between these variables to capture multi-level relationships at short and long horizons. The major findings are summarized as follows. First, from the multi-scale decomposition analysis, we identify that Granger causality test results and the direction and strength of return spillovers change with the level of decomposition. Second, the results of nonlinear causality tests show variation in both the significance and direction of Granger causality relationships between the decomposed currency and other series at different timescales, especially for the decomposed oil, gold, and OVX series. Third, the measured directional spillover indices identify the Euro–Dollar exchange rate as the largest contributor of connectedness to the other series.

Energy exchanges in the flow past a cylinder with a leeward control rod

We study the energy exchanges between coherent structures and the mean flow in the wake of a cylinder in the presence of a leeward control rod using particle image velocimetry data at Reynolds number ( $Re$ ) $20\times 10^3$ . The shedding of the control rod depends on the oncoming shear layer and hence the downstream interaction of the main cylinder's and control rod's wake strongly depends on the control rod's setting angle ( $\theta$ ). In this work we study this interaction between the shedding modes from the cylinder and control rod at different $\theta$ . New secondary coherent motions with distinct characteristic frequencies appear in the flow field aside from the frequencies associated with the sheddings of the control rod, the main cylinder and its harmonics. A multiscale triple decomposition method is applied to extract the coherent modes associated with each of these frequencies, and the dynamics of the modes are studied using kinetic energy budget equations. The primary shedding modes of the control rod and main cylinder, as well as the harmonics of the main cylinder's shedding modes, are found to be primarily energised by the mean flow at this $Re$ , while the secondary modes are almost entirely energised by the primary modes, similar to the findings of Baj & Buxton (Phys. Rev. Fluids, vol. 2, issue 11, 2017, 114607) for a different multiscale flow configuration. The remarkable similarity in the energy exchange process forming the secondary coherent modes, observed in two different studies, hints at a possible universality in the formation process of these secondary structures in a multiscale flow.

Daily streamflow forecasting using hybrid long short-term memory model

Characterized by heterogeneity, complexity and non-stationary, streamflow forecasting has always been a challenge in hydrological sciences. In this study, a multiscale wavelet decomposition method with long short-term memory model (WLSTM) is developed to handle the daily streamflow forecasting. Discrete wavelet transform (DWT) is employed to extract multiscale features, which are then simulated by long short-term memory models (LSTMs), respectively. The outputs of the different scales LSTMs are finally reconstructed toward the forecasting results. Seven years daily streamflow sequences of three tributaries and one mainstream in the upper reaches of the Yangtze River are investigated by the WLSTM models. For comparison, standard LSTM, and traditional ANN are chosen for the same streamflow forecasting task. Experimental results show that the proposed hybrid model is better than other comparison models in terms of evaluation indicators. Considering that large floods often occur in the Yangtze River Basin, the performance of flood forecasting is also analyzed and the result shows that forecasting errors of WLSTM are more concentrated, which means WLSTM outperforms traditional ANN and LSTM for the extreme flood forecasting.

Industrial gearbox fault diagnosis based on multi-scale convolutional neural networks and thermal imaging

Infrared thermal technology plays a vital role in the health condition monitoring of gearbox. In the traditional infrared thermal technology-based methods, Gaussian pyramid is applied as the feature extraction approach, which has disadvantages of noise influence and information missing. Focus on such disadvantages, an improved multi-scale decomposition method combined with convolutional neural network is proposed to extract the fault features of the multi-scale infrared images in this paper. It can enlarge the data length at large scales, and thus reduce the fluctuations of feature values and reserve the fault information. The effectiveness of the proposed method is validated using the experiment infrared data of one industrial gearbox. Results demonstrate that our proposed method has the best performance comparing with five methods.