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Multi-Focus Image Fusion Method Based on Multi-Scale Decomposition of Information Complementary [PDF]
Entropy, 2021 Multi-focus image fusion is an important method used to combine the focused parts from source multi-focus images into a single full-focus image. Currently, to address the problem of multi-focus image fusion, the key is on how to accurately detect the ...
Hui Wan +3 more
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IEEE Access, 2017 Image fusion is a well-recognized and a conventional field of image processing. Image fusion provides an efficient way of enhancing and combining pixel-level data resulting in highly informative data for human perception as compared with individual input
Ayush Dogra, Bhawna Goyal, Sunil Agrawal
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A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis
Proceedings of the VLDB Endowment, 2023 Time series data, including univariate and multivariate ones, are characterized by unique composition and complex multi-scale temporal variations. They often require special consideration of decomposition and multi-scale modeling to analyze.
Shuhan Zhong +5 more
semanticscholar +5 more sources
Parameter‐adaptive nighttime image enhancement with multi‐scale decomposition
IET Computer Vision, 2016 As a challenging problem, image enhancement plays an important role in computer vision applications and has been widely studied. As one of the most difficult issues of image enhancement, outdoor nighttime image enhancement suffers from noise ...
Shuhang Wang, Jin Zheng, Bo Li
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IEEE Access, 2021 Numerous studies employ multi-scale decomposition to improve the prediction performance of neural networks, but the grounds for selecting the decomposition algorithm are not explained, and the effects of decomposition algorithms on other performance of ...
Haichao Huang +4 more
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AMDCnet: attention-gate-based multi-scale decomposition and collaboration network for long-term time series forecasting [PDF]
Frontiers in Artificial IntelligenceIntroductionTime series analysis plays a critical role in various applications, including sensor data monitoring, weather forecasting, economic predictions, and network traffic management. While traditional methods primarily focus on modeling time series
Shikang Hou +4 more
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Journal of Marine Science and Engineering, 2023 Visual object detection is an essential task for the intelligent navigation of an Unmanned Surface Vehicle (USV), which can sense the obstacles while navigating.
Zhiguo Zhou +4 more
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Systematic multi-scale decomposition of ocean variability using machine learning
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2022 Multi-scale systems, such as the climate system, the atmosphere, and the ocean, are hard to understand and predict due to their intrinsic nonlinearities and chaotic behavior. Here, we apply a physics-consistent machine learning method, the multi-resolution dynamic mode decomposition (mrDMD), to oceanographic data.
Christian L. E. Franzke +2 more
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Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting
Proceedings of the AAAI Conference on Artificial IntelligenceTransformer-based and MLP-based methods have emerged as leading approaches in time series forecasting (TSF). However, real-world time series often show different patterns at different scales, and future changes are shaped by the interplay of these overlapping scales, requiring high-capacity models.
Yifan Hu 0006 +4 more
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Unified framework for multi‐scale decomposition and applications
The Journal of Engineering, 2017 Since real‐world digital images differ in thousands ways, an adaptive multi‐scale decomposition scheme adapting to images is increasingly urgently required for image analysis and applications. In this paper, a unified framework for multi‐scale decomposition is developed.
Xu Guanlei +4 more
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