使用超像素分割与图割的网状遮挡物检测算法

摘要

针对由于摄影角度受限,一些自然图像被铁丝网、栅栏、外墙玻璃接缝等网状遮挡物所遮挡的问题,提出了一种用于修复此类图像的网状遮挡物检测算法.对于现有算法使用单像素颜色特征和固定形状特征造成对颜色和形状不均的网状遮挡物检测效果不佳的弊端,首先将图像进行超像素分割,引入颜色与纹理直方图的联合特征来描述超像素块,将基于像素分类问题转换成基于超像素的分类问题,抑制了局部颜色变化造成的误分类;然后,使用图割算法将超像素块进行分类,使网状结构能够沿着光滑的边缘进行延伸,不受固定的形状限制,提高了对异形网状结构的检测准确率,并且不依赖Farid等提出的算法(FARID M S,MAHMOOD A,GRANGETTO M.Image de-fencing framework with hybrid inpainting algorithm.Signal,Image and Video Processing,2016,10(7):1193-1201)所需的人工输入;其次使用新的联合特征训练支持向量机(SVM)分类器并对所有未被分类的超像素块进行分类,得到准确网状遮挡物掩膜;最后,使用SAIST算法对图像进行修复.实验中,获得的网状遮挡物掩膜比Farid等提出的算法所得到的保留了更多的细节,在修复算法不变的同时显著提升了图像修复效果.在使用相同网状遮挡物掩膜的情况下,使用SAIST算法修复得到的图片在峰值信噪比(PSNR)和结构相似性(SSIM)上分别比Farid等提出算法提高了3.06和0.02.新的掩膜检测算法联合SAIST修复算法的总体修复效果对比Farid等提出算法及Liu等提出的算法(LIUY Y,BELKINA T,HAYS J H,et al.Image de-fencing.Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition.Washington,DC:IEEE Computer Society,2008:1-8)有了明显提升.实验结果表明,所提算法提升了网状遮挡物的检测准确性,得到了效果更好的去除网状遮挡物的图像.%Due to the limited angle of photography,some natural images are oscured by fence-like occlusion such as barbed wire,fence and glass joints.A novel fence-like occlusion detection algorithm was proposed to repair such images.Firstly,aiming at the drawbacks of the existing fence detection algorithms using single pixel color feature and fixed shape feature,the image was divided into super pixels and a joint feature of color and texture was introduced to describe the super pixel blocks.Thus,the classification of a pixel classification problem was converted to a super pixel classification problem,which inhibited the misclassification caused by local color changes.Secondly,the super-pixel blocks were classified by using the graph cuts algorithm to extend the mesh structure along the smooth edges without being restricted by the fixed shape,which improved the detection accuracy of the special-shaped fence structure and avoided the manual input required by the algorithm proposed by Farid et al.(FARID M S,MAHMOOD A,GRANGETTO M.Image de-fencing framework with hybrid inpainting algorithm.Signal,Image and Video Processing,2016,10(7):1193-1201) Then,new joint features were used to train the Support Vector Machine (SVM) classifier and classify all non-classified super-pixel blocks to obtain an accurate fence mask.Finally,the SAIST (Spatially Adaptive Iterative Singular-value Thresholding) inpainting algorithm was used to repair the image.In the experiment,the obtained fence mask retained more detail than that of the algorithm proposed by Farid et al.,meanwhile using the same repair algorithm,the image restoration effect was significantly improved.Using the same fence mask,restored images by using the SAIST algorithm are 3.06 and 0.02 higher than that by using the algorithm proposed by Farid et al.,respectively,in Peak Signal-to-Noise Rate (PSNR) and Structural SIMilarity (SSIM).The overall repair results were significantly improved compared to the algorithm proposed by Farid et al.and the algorithm proposed by Liu et al.(LIU Y Y,BELKINA T,HAYS J H,et al.Image de-fencing.Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition.Washington,DC:IEEE Computer Society,2008:1-8) when using the SAIST inpainting algorithm combined with the proposed fence detection algorithm.The experimental results show that the proposed algorithm improves the detection accuracy of the fence mask,thus yields better fence removed image reconstruction.