A Spatial Fusion Scheme of Multi-focus Image Combining SVM-Based Classification and PCA-Based Weight

Yang, Yikun; Jiang, Qian; Yao, Shaowen; Xue, Gang; Wu, Liwen; Jin, Xin

doi:10.1007/978-3-030-32456-8_42

Cited by 2 publications

(1 citation statement)

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“…The local features of the source images are regarded as a key factor in multi-focus image fusion. Thus, a novel image fusion method based on PCA joint sliding window is employed to fuse the source images, in which the fusion weight of each pixel in the dispute area is calculated [32]. Since each size of the sliding window only reflects the regional features in a single-scale, the windows with different sizes are simultaneously combined with PCA to get the corresponding fusion weights.…”

Section: Our Proposed Multiscale Image Fusion Methods Based On Pcamentioning

confidence: 99%

Multi-Sensor Image Fusion Using Optimized Support Vector Machine and Multiscale Weighted Principal Component Analysis

et al. 2020

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Multi-sensor image fusion is used to combine the complementary information of source images from the multiple sensors. Recently, conventional image fusion schemes based on signal processing techniques have been studied extensively, and machine learning-based techniques have been introduced into image fusion because of the prominent advantages. In this work, a new multi-sensor image fusion method based on the support vector machine and principal component analysis is proposed. First, the key features of the source images are extracted by combining the sliding window technique and five effective evaluation indicators. Second, a trained support vector machine model is used to extract the focus region and the non-focus region of the source images according to the extracted image features, the fusion decision is therefore obtained for each source image. Then, the consistency verification operation is used to absorb a single singular point in the decisions of the trained classifier. Finally, a novel method based on principal component analysis and the multi-scale sliding window is proposed to handle the disputed areas in the fusion decision pair. Experiments are performed to verify the performance of the new combined method.

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Section: Our Proposed Multiscale Image Fusion Methods Based On Pcamentioning

confidence: 99%

Multi-Sensor Image Fusion Using Optimized Support Vector Machine and Multiscale Weighted Principal Component Analysis

et al. 2020

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Vocal Intelligence Training System for Music Performance Majors based on Multi-point Signal Recognition and Fusion

Zhao

2022

2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS)

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A Spatial Fusion Scheme of Multi-focus Image Combining SVM-Based Classification and PCA-Based Weight

Cited by 2 publications

References 20 publications

Multi-Sensor Image Fusion Using Optimized Support Vector Machine and Multiscale Weighted Principal Component Analysis

Multi-Sensor Image Fusion Using Optimized Support Vector Machine and Multiscale Weighted Principal Component Analysis

Vocal Intelligence Training System for Music Performance Majors based on Multi-point Signal Recognition and Fusion

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