IEEE Trans Image Process. 2017 Jan;26(1):160-171. doi: 10.1109/TIP.2016.2621413.
We address the removal of canvas artifacts from high-resolution digital photographs and X-ray images of paintings on canvas. Both imaging modalities are common investigative tools in art history and art conservation. Canvas artifacts manifest themselves very differently according to the acquisition modality; they can hamper the visual reading of the painting by art experts, for instance, in preparing a restoration campaign. Computer-aided canvas removal is desirable for restorers when the painting on canvas they are preparing to restore has acquired over the years a much more salient texture. We propose a new algorithm that combines a cartoon-texture decomposition method with adaptive multiscale thresholding in the frequency domain to isolate and suppress the canvas components. To illustrate the strength of the proposed method, we provide various examples, for acquisitions in both imaging modalities, for paintings with different types of canvas and from different periods. The proposed algorithm outperforms previous methods proposed for visual photographs such as morphological component analysis and Wiener filtering and it also works for the digital removal of canvas artifacts in X-ray images.
我们解决了从高分辨率数字照片和画布上绘画的 X 射线图像中去除画布伪影的问题。这两种成像方式都是艺术史和艺术保护领域常用的研究工具。画布伪影根据获取方式表现出非常不同的特征；它们可能会干扰艺术专家对画作的视觉解读，例如在准备修复活动时。当准备修复的画布上的画作随着时间的推移获得了更明显的纹理时，对于修复师来说，计算机辅助的画布去除是可取的。我们提出了一种新的算法，该算法将卡通纹理分解方法与频域中的自适应多尺度阈值处理相结合，以分离和抑制画布成分。为了说明所提出方法的优势，我们提供了各种示例，包括两种成像方式的采集、具有不同类型画布和来自不同时期的画作。与之前针对视觉照片提出的方法（如形态成分分析和维纳滤波）相比，所提出的算法表现更好，并且它也适用于 X 射线图像中画布伪影的数字去除。
