基于加权优先级和分类匹配的图像修复方法

待修复像素优先级的计算及最佳匹配块的确定是基于纹理合成图像修复方法的两个基本环节,传统方法不仅难于确定优先级计算中的置信度,而且难于搜索到最佳匹配块.提出了一种基于加权优先级和分类匹配的图像修复方法,该方法在优先级模型中,引入指数函数和正规化函数分别优化置信度和数据项,使得计算的优先级更加客观,从而使修复顺序更加合理.基于此,将结构信息作为搜索匹配块的一个度量因子,采用分类筛选方式,选取最佳匹配块.实验结果表明,所提方法在获得良好修复效果的前提下缩短了修复时间.     

f+v分解与分形图像修复算法设计

提出了一种基于分形与图像分解的修复算法,将图像分解成为纹理和结构两部分,纹理部分采用以分形维度作为度量函数的修复算法,结构部分采用曲率驱动扩散CDD(Curvature-Drive Diffusions)算法进行并行修复,达到快速、高效的修复受损图像的目的。结果表明,该算法具有速度快、对大面积受损图像修复效果好的特点。     

基于结构分量和信息熵的Criminisi图像修复算法

针对Criminisi图像修复算法中优先级计算易受 图像纹理影响的问题,提出了改 进的基于图像结构分量的优先级函数。首先采用变分分解模型,将待修补图像分解为结构分 量和 纹理分量;其次基于结构分量计算数据项,排除纹理的影响;然后在优先权函数中 引入度量像素块复杂度的信息熵,将像素块中除了中心点之外其它位置的结构信息 融 入到优先权的计算中,使修补次序进一步向结构丰富的像素块倾斜;最后将优先权函数 表 示为置信度、数据项和信息熵的加权和,以解决传统Criminisi算法优先权随着置信度 迅速 下降为零而造成修复次序出现偏差的不足。新的优先权函数排除了像素块中在计算数据项时 纹 理的影响,并且融合更多的结构信息,使修复次序更加准确。实验结果表明,对于 不 同的人工图像和自然图像,本文模型都能取得较为满意的修复结果。     

Virtual view synthesis using layered depth image generation and depth-based inpainting for filling disocclusions and translucent disocclusions

View synthesis is an efficient solution to produce content for 3DTV and FTV. However, proper handling of the disocclusions is a major challenge in the view synthesis. Inpainting methods offer solutions for handling disocclusions, though limitations in foreground-background classification causes the holes to be filled with inconsistent textures. Moreover, the state-of-the art methods fail to identify and fill disocclusions in intermediate distances between foreground and background through which background may be visible in the virtual view (translucent disocclusions). Aiming at improved rendering quality, we introduce a layered depth image (LDI) in the original camera view, in which we identify and fill occluded background so that when the LDI data is rendered to a virtual view, no disocclusions appear but views with consistent data are produced also handling translucent disocclusions. Moreover, the proposed foreground-background classification and inpainting fills the disocclusions with neighboring background texture consistently. Based on the objective and subjective evaluations, the proposed method outperforms the state-of-the art methods at the disocclusions.     

Detecting image seam carving with low scaling ratio using multi-scale spatial and spectral entropies

Seam carving is the most popular content-aware image retargeting technique. However, it may also be used to correct poor photo composition in photography competition or to remove object from image for malicious purpose. A blind detection approach is presented for seam carved image with low scaling ratio (LSR). It exploits spatial and spectral entropies (SSE) on multi-scale images (candidate image and its down-sampled versions). We observe that when a few seams are deleted from an original image, its SSE distribution is greatly changed. Forty-two features are designed to unveil the statistical properties of SSE in terms of centralized tendency, dispersion tendency and distribution tendency. They are combined with the local binary pattern (LBP)-based energy features to form ninety-six features. Finally, support vector machine (SVM) is exploited as classifier to determine whether an image is original or suffered from seam carving. Experimental results show that the proposed approach achieves superior detection accuracy over the state-of-the-art works, especially for resized image by seam carving with LSRs. Moreover, it is robust against JPEG compression and seam insertion.