Multiscale Decomposition Approach Research Articles

Fidelity based visual compensation and salient information rectification for infrared and visible image fusion

The fusion technology, combining infrared and visible modes, has the potential to enhance the semantic content of backgrounds, thereby improving scene interpretability. However, most existing image fusion algorithms primarily concentrate on the fusion process, often neglecting the importance of preprocessing the source images to enhance their visual fidelity. Additionally, these algorithms frequently overlook the distinct characteristics of infrared and visible modes, leading to suboptimal weight allocations that do not correspond with human perception. To tackle these issues, this paper proposes a fusion algorithm that emphasizes visual fidelity and the rectification of salient information. More specifically, we improve fusion algorithms by designing an adaptive enhancement method based on Taylor approximation and visual compensation, which proves particularly effective in complex environments. Our proposed multi-scale decomposition approach extracts salient information from the transmission map, thereby enriching fusion results with finer details to accentuate target features. Drawing inspiration from the distinctive attributes of infrared and visible image modes, we devise a fusion weight calculation method grounded in similarity measurements to effectively convey significant information from the source images. To validate the effectiveness of our proposed method, we conducted validation experiments using publicly available datasets. Our experimental findings exhibit a prominent advantage over fifteen state-of-the-art fusion algorithms in both subjective and objective assessments. Our code is publicly available at: https://github.com/VCMHE/FVC_SIR.

Is oil-gas price decoupling happening in China? A multi-scale quantile-on-quantile approach

China's natural gas market is not only growing rapidly but is also in the process of liberalization, indicating the potential of oil-gas price decoupling. In this situation, this study re-examines whether oil-gas price decoupling is happening in China, in order to analyze the achievements of market reform. First, a multi-scale decomposition approach is used to find oil-gas price patterns in the time-frequency domain. Then, a quantile-on-quantile analysis is used to acquire the entire dependence structure between oil and gas prices. The findings indicate that the association between oil and gas prices increases over scales but varies for quantiles. Specifically, clear evidence is provided that China's liquefied natural gas (LNG) price is independent of the oil prices selected for this study in the short term, whereas no additional evidence exists that proves oil and gas prices have already been decoupled. This finding indicates that the oil-indexation pricing mechanism still remains dominant. Based on the empirical results, the authors suggest that the establishment of trading hubs, the removal of restrictive clauses in contracts, and the diversification of pricing mechanisms should be simultaneously promoted in China.

Identification of relevant wavelet functions for multiscale characterization of manufactured surfaces using a genetically optimized neural network

Multiscale surface characterization is a powerful tool that is used for process monitoring, surface quality control, and manufacturing optimization by establishing a link between process variables and functional performances. The multiscale decomposition approach using continuous and discrete wavelets is widely applied to take into account scales dependency. However, the optimal choice of the analysis wavelet function and the number of decomposition level is still a big issue. In this article, the artificial neural network theory was combined with the wavelet concept and was optimized based on the genetic algorithm to identify the relevant wavelet function for multiscale characterization of abraded surface topographies. Then, an extensive wavelet function library was developed and the proposed algorithm was applied to topographic data obtained from various abrasive finishing processes. The results show the pertinence of this approach to select the relevant wavelet, and a universal relevant wavelet function for abraded surface characterization was determined.

Nonlinear Fusion of Multispectral Citrus Fruit Image Data with Information Contents.

The main issue of vison-based automatic harvesting manipulators is the difficulty in the correct fruit identification in the images under natural lighting conditions. Mostly, the solution has been based on a linear combination of color components in the multispectral images. However, the results have not reached a satisfactory level. To overcome this issue, this paper proposes a robust nonlinear fusion method to augment the original color image with the synchronized near infrared image. The two images are fused with Daubechies wavelet transform (DWT) in a multiscale decomposition approach. With DWT, the background noises are reduced and the necessary image features are enhanced by fusing the color contrast of the color components and the homogeneity of the near infrared (NIR) component. The resulting fused color image is classified with a C-means algorithm for reconstruction. The performance of the proposed approach is evaluated with the statistical F measure in comparison to some existing methods using linear combinations of color components. The results show that the fusion of information in different spectral components has the advantage of enhancing the image quality, therefore improving the classification accuracy in citrus fruit identification in natural lighting conditions.

Contrast Enhanced Low-light Visible and Infrared Image Fusion

<p>Multi-modal image fusion objective is to combine complementary information obtained from multiple modalities into a single representation with increased reliability and interpretation. The images obtained from low-light visible cameras containing fine details of the scene and infrared cameras with high contrast details are the two modalities considered for fusion. In this paper, the low-light images with low target contrast are enhanced by using the phenomenon of stochastic resonance prior to fusion. Entropy is used as a measure to tune iteratively the coefficients using bistable system parameters. The combined advantage of multi scale decomposition approach and principal component analysis is utilized for the fusion of enhanced low-light visible and infrared images. Experimental results were carried out on different image datasets and analysis of the proposed methods were discussed. </p>

A multiscale decomposition approach to detect abnormal vasculature in the optic disc

This paper presents a multiscale method to detect neovascularization in the optic disc (NVD) using fundus images. Our method is applied to a manually selected region of interest (ROI) containing the optic disc. All the vessels in the ROI are segmented by adaptively combining contrast enhancement methods with a vessel segmentation technique. Textural features extracted using multiscale amplitude-modulation frequency-modulation, morphological granulometry, and fractal dimension are used. A linear SVM is used to perform the classification, which is tested by means of 10-fold cross-validation. The performance is evaluated using 300 images achieving an AUC of 0.93 with maximum accuracy of 88%.

A multiscale mass scaling approach for explicit time integration using proper orthogonal decomposition

SUMMARYOne of the main computational issues with explicit dynamics simulations is the significant reduction of the critical time step as the spatial resolution of the finite element mesh increases. In this work, a selective mass scaling approach is presented that can significantly reduce the computational cost in explicit dynamic simulations, while maintaining accuracy. The proposed method is based on a multiscale decomposition approach that separates the dynamics of the system into low (coarse scales) and high frequencies (fine scales). Here, the critical time step is increased by selectively applying mass scaling on the fine scale component only. In problems where the response is dominated by the coarse (low frequency) scales, significant increases in the stable time step can be realized. In this work, we use the proper orthogonal decomposition (POD) method to build the coarse scale space. The main idea behind POD is to obtain an optimal low‐dimensional orthogonal basis for representing an ensemble of high‐dimensional data. In our proposed method, the POD space is generated with snapshots of the solution obtained from early times of the full‐scale simulation. The example problems addressed in this work show significant improvements in computational time, without heavily compromising the accuracy of the results. Copyright © 2013 John Wiley & Sons, Ltd.