Xu Ruotao, Xu Yong, Quan Yuhui
IEEE Trans Image Process. 2021;30:1542-1555. doi: 10.1109/TIP.2020.3043665. Epub 2021 Jan 5.
Morphology component analysis provides an effective framework for structure-texture image decomposition, which characterizes the structure and texture components by sparsifying them with certain transforms respectively. Due to the complexity and randomness of texture, it is challenging to design effective sparsifying transforms for texture components. This paper aims at exploiting the recurrence of texture patterns, one important property of texture, to develop a nonlocal transform for texture component sparsification. Since the plain patch recurrence holds for both cartoon contours and texture regions, the nonlocal sparsifying transform constructed based on such patch recurrence sparsifies both the structure and texture components well. As a result, cartoon contours could be wrongly assigned to the texture component, yielding ambiguity in decomposition. To address this issue, we introduce a discriminative prior on patch recurrence, that the spatial arrangement of recurrent patches in texture regions exhibits isotropic structure which differs from that of cartoon contours. Based on the prior, a nonlocal transform is constructed which only sparsifies texture regions well. Incorporating the constructed transform into morphology component analysis, we propose an effective approach for structure-texture decomposition. Extensive experiments have demonstrated the superior performance of our approach over existing ones.
形态学成分分析为结构 - 纹理图像分解提供了一个有效的框架，该框架通过分别使用特定变换对结构和纹理成分进行稀疏化来表征它们。由于纹理的复杂性和随机性，为纹理成分设计有效的稀疏化变换具有挑战性。本文旨在利用纹理模式的重复性（纹理的一个重要属性）来开发一种用于纹理成分稀疏化的非局部变换。由于普通补丁重复性对卡通轮廓和纹理区域都成立，基于这种补丁重复性构建的非局部稀疏化变换能很好地稀疏化结构和纹理成分。结果，卡通轮廓可能会被错误地分配到纹理成分中，导致分解出现模糊性。为了解决这个问题，我们引入了一种关于补丁重复性的判别先验，即纹理区域中重复补丁的空间排列呈现出与卡通轮廓不同的各向同性结构。基于该先验，构建了一种仅能很好地稀疏化纹理区域的非局部变换。将构建的变换纳入形态学成分分析中，我们提出了一种有效的结构 - 纹理分解方法。大量实验证明了我们的方法优于现有方法。
