Learning to Remove Soft Shadows
Learning to Remove Soft Shadows
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DOI：
10.1145/2732407
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发表时间：
2015-10-01
影响因子：
6.2
通讯作者：
Brostow, Gabriel J.
中科院分区：
文献类型：
--
作者：
Gryka, Maciej;Terry, Michael;Brostow, Gabriel J.
如果用户的编辑没有考虑到阴影，被操纵的图像就会失去可信度。我们提出了一种方法，使去除和编辑软阴影容易。软阴影无处不在，但仍然难以提取和处理。我们假设软阴影可以被分割，因此可以编辑，通过学习生成阴影的图像补丁的映射函数。我们通过仅使用少量用户输入就从照片中去除柔和阴影来验证这一前提。只要给定广泛的用户笔触来指示要处理的区域，我们的新监督回归算法就会自动去除图像的阴影，去除本影和半影。由此产生的照明图像经常被认为是一个可信的无阴影版本的场景。我们在一大组软阴影图像上测试了这种方法，并进行了一项用户研究，将我们的方法与最先进的方法和真实的灯光场景进行了比较。我们的结果更难以识别为被改变，被认为比以前的工作更可取。
Manipulated images lose believability if the user's edits fail to account for shadows. We propose a method that makes removal and editing of soft shadows easy. Soft shadows are ubiquitous, but remain notoriously difficult to extract and manipulate. We posit that soft shadows can be segmented, and therefore edited, by learning a mapping function for image patches that generates shadow mattes. We validate this premise by removing soft shadows from photographs with only a small amount of user input.Given only broad user brush strokes that indicate the region to be processed, our new supervised regression algorithm automatically unshadows an image, removing the umbra and penumbra. The resulting lit image is frequently perceived as a believable shadow-free version of the scene. We tested the approach on a large set of soft shadow images, and performed a user study that compared our method to the state-of-the-art and to real lit scenes. Our results are more difficult to identify as being altered and are perceived as preferable compared to prior work.