Sur Frederic
IEEE Trans Image Process. 2018 Nov 19. doi: 10.1109/TIP.2018.2881906.
This paper addresses the problem of cartoon and texture decomposition. Microtextures being characterized by their power spectrum, we propose to extract cartoon and texture components from the information provided by the power spectrum of image patches. The contribution of texture to the spectrum of a patch is detected as statistically significant spectral components with respect to a null hypothesis modeling the power spectrum of a non-textured patch. The null-hypothesis model is built upon a coarse cartoon representation obtained by a basic yet fast filtering algorithm of the literature. Hence the term "dual domain": the coarse decomposition is obtained in the spatial domain and is an input of the proposed spectral approach. The statistical model is also built upon the power spectrum of patches with similar textures across the image. The proposed approach therefore falls within the family of non-local methods. Experimental results are shown in various application areas, including canvas pattern removal in fine arts painting, or periodic noise removal in remote sensing imaging.
本文探讨了卡通与纹理分解问题。微纹理通过其功率谱来表征，我们建议从图像块功率谱提供的信息中提取卡通和纹理成分。相对于对无纹理块功率谱进行建模的零假设，纹理对块频谱的贡献被检测为具有统计显著性的频谱成分。零假设模型基于通过文献中一种基本但快速的滤波算法获得的粗略卡通表示构建。因此有了“双域”这个术语：粗略分解在空间域中获得，并且是所提出的频谱方法的一个输入。统计模型也基于图像中具有相似纹理的块的功率谱构建。因此，所提出的方法属于非局部方法家族。实验结果展示在各种应用领域，包括美术绘画中画布图案去除，或遥感成像中周期性噪声去除。
