一种新的基于多尺度卷积的高斯-拉普拉斯算子用于舞蹈运动图像增强

IF 1.1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Dianhuai Shen, X. Jiang, Lin Teng
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引用次数: 8

摘要

传统的图像增强方法存在对比度低、细节模糊等问题。因此,我们提出了一种新的基于多尺度卷积的高斯-拉普拉斯算子用于舞蹈运动图像增强。首先,采用多尺度卷积对图像进行预处理。然后,对传统的拉普拉斯边缘检测算子进行改进,将其与高斯滤波相结合。采用高斯滤波对图像进行平滑处理和噪声抑制,边缘检测采用拉普拉斯梯度边缘检测器进行处理。将高斯拉普拉斯算子提取的细节图像与亮度增强后的图像进行线性加权融合,重建出细节边缘清晰、对比度强的图像。用不同场景下的详细图像进行了实验。通过与传统方法的比较,验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement
Traditional image enhancement methods have the problems of low contrast and fuzzy details. Therefore, we propose a novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement. Firstly, multi-scale convolution is used to preprocess the image. Then, we improve the traditional Laplace edge detection operator and combine it with Gauss filter. The Gaussian filter is used to smooth the image and suppress the noise, and the edge detection is processed based on the Laplace gradient edge detector. The detail image extracted by Gauss-Laplace operator and the image with brightness enhancement are linearly weighted fused to reconstruct the image with clear detail edge and strong contrast. Experiments are carried out with detailed images in different scenes. It is compared with traditional methods to verify the effectiveness of the proposed method.
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来源期刊
EAI Endorsed Transactions on Scalable Information Systems
EAI Endorsed Transactions on Scalable Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.80
自引率
15.40%
发文量
49
审稿时长
10 weeks
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