利用改进的超像素分割和噪声估计的图像拼接篡改定位方法

通过检测图像局部噪声水平的不一致性,提出一种图像拼接篡改区域的定位方法.首先,用改进的简单线性迭代聚类(SLIC)超像素分割算法将待检测图像分割成具有相似特征的像素块;然后,采用基于主成分分析的噪声水平估计方法计算每个图像块的局部噪声水平;最后,利用3种聚类算法对估算的噪声水平进行聚类,根据聚类结果定位出被篡改的区域.实验结果表明:文中方法不仅能有效定位被篡改的区域,而且能保留检测区域更多的边缘信息.

Image Splicing Tampered Localization Method Using ImprovedSuperpixel Segmentation and Noise Estimation

By detecting the inconsistency of the local noise level of the image, this paper proposes a method to locate the image splicing tampered region. Firstly, the improved simple linear iterative clustering(SLIC)superpixel segmentation algorithm is used to segment the detection image into pixel blocks with similar features. Secondly, the noise level estimation based on principal component analysis is used to calculate the local noise level of each image block. Finally, three clustering algorithms are used to cluster the estimated noise level results. The tampered region of the image is located according to the clustering results. The experimental results show that the method in this paper can effectively locate the tampered region and retain more edge information of the detection region.