Intelligent extraction of salient objects from fuzzy distorted images based on multi-scale convolution.
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| Title: | Intelligent extraction of salient objects from fuzzy distorted images based on multi-scale convolution. |
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| Authors: | Lu, Lingling1 (AUTHOR) lull621814@163.com |
| Source: | Imaging Science Journal. Jun2025, Vol. 73 Issue 4, p401-414. 14p. |
| Subjects: | Problem solving, Pyramids |
| Abstract: | To solve the problem of how to obtain important information from fuzzy distorted images, an intelligent method for extracting salient objects from fuzzy distorted images based on multi-scale convolution is proposed. A multi-scale deformation feature convolution network is established to extract the salient features of fuzzy distorted images. Among them, the multi-scale convolution network VGG-16 extracts the shallow features of the fuzzy distortion image through the convolution layer operation, uses the multi-scale convolution kernel to extract the fuzzy distortion image in parallel, and introduces the self-attention mechanism to make the extracted features more relevant, Input the fused features into the proposed area extraction network, Through the target area detection network, the proposed target area of the fuzzy distortion image is classified and position regression is performed to obtain the final salient target of the fuzzy distortion image. The experimental results show that this method can accurately recognize the contour of salient objects, and extract salient objects from fuzzy distorted images; The extracted salient objects have high accuracy, low mean square error and similarity of 92.9% with the original image; For the salient target extraction in various scenarios, it shows high advantages. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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