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Faithful Recovery of Vector Valued Functions from Incomplete Data

机译:从不完整数据中忠实地恢复向量值函数

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摘要

On March 11, 1944, the famous Eremitani Church in Padua (Italy) was destroyed in an Allied bombing along with the inestimable frescoes by Andrea Mantegna et al. contained in the Ovetari Chapel. In the last 60 years, several attempts have been made to restore the fresco fragments by traditional methods, but without much success. We have developed an efficient pattern recognition algorithm to map the original position and orientation of the fragments, based on comparisons with an old gray level image of the fresco prior to the damage. This innovative technique allowed for the partial reconstruction of the frescoes. Unfortunately, the surface covered by the fragments is only 77 m~2, while the original area was of several hundreds. This means that we can reconstruct only a fraction (less than 8%) of this inestimable artwork. In particular the original color of the blanks is not known. This begs the question of whether it is possible to estimate mathematically the original colors of the frescoes by making use of the potential information given by the available fragments and the gray level of the pictures taken before the damage. Can one estimate how faithful such restoration is? In this paper we retrace the development of the recovery of the frescoes as an inspiring and challenging real-life problem for the development of new mathematical methods. We introduce two models for the recovery of vector valued functions from incomplete data, with applications to the fresco recolorization problem. The models are based on the minimization of a functional which is formed by the discrepancy with respect to the data and additional regularization constraints. The latter refer to joint spar-sity measures with respect to frame expansions for the first functional and functional total variation for the second. We establish the relations between these two models. As a byproduct we develop the basis of a theory of fidelity in color recovery, which is a crucial issue in art restoration and compression.
机译:1944年3月11日,安德里亚·曼特尼亚(Andrea Mantegna)等人在一次盟军的炸弹袭击中,连同无法估量的壁画摧毁了帕多瓦(意大利)著名的Eremitani教堂。包含在Ovetari教堂中。在过去的60年中,已经进行了几次尝试通过传统方法修复壁画碎片的尝试,但是都没有成功。基于与损坏前壁画的旧灰度图像的比较,我们已经开发了一种有效的模式识别算法,以绘制碎片的原始位置和方向。这项创新技术使壁画得以部分重建。不幸的是,碎片覆盖的表面只有77 m〜2,而原始区域只有几百个。这意味着我们只能重建此不可估量艺术品的一小部分(少于8%)。尤其是毛坯的原始颜色是未知的。这就产生了一个问题,即是否有可能通过利用可用碎片给出的潜在信息和损坏前所拍摄图片的灰度来数学估算壁画的原始颜色。可以估计这样的修复有多忠实吗?在本文中,我们追溯了壁画回收的发展,将其作为启发性和挑战性的现实问题来开发新的数学方法。我们介绍了两种用于从不完整数据中恢复向量值函数的模型,并将其应用于壁画重着色问题。这些模型基于功能的最小化,该功能是由数据方面的差异和其他正则化约束形成的。后者是指针对第一个功能的帧扩展和第二个功能的总变化量的联合稀疏性度量。我们建立这两个模型之间的关系。作为副产品,我们开发了色彩恢复保真度理论的基础,这是艺术恢复和压缩中的关键问题。

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