A novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement

EAI Endorsed Transactions on Scalable Information Systems

article

Dianhuai Shen¹  Xueying Jiang²  Lin Teng³ 

[1] College of Music and Dance, Huaqiao University;School of Public Policy and Management, Tsinghua University;Software College, Shenyang Normal University

关键词: dance image enhancement;    Gauss-Laplace operator;    multi-scale convolution;   

DOI  :  10.4108/eai.17-12-2021.172439

学科分类：社会科学、人文和艺术（综合）

来源: Bern Open Publishing

【 摘 要 】

This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173797. 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.

【 授权许可】

CC BY