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User-assisted intrinsic images

机译:用户辅助的内在图像

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For many computational photography applications, the lighting and materials in the scene are critical pieces of information. We seek to obtain intrinsic images, which decompose a photo into the product of an illumination component that represents lighting effects and a reflectance component that is the color of the observed material. This is an under-constrained problem and automatic methods are challenged by complex natural images. We describe a new approach that enables users to guide an optimization with simple indications such as regions of constant reflectance or illumination. Based on a simple assumption on local reflectance distributions, we derive a new propagation energy that enables a closed form solution using linear least-squares. We achieve fast performance by introducing a novel downsampling that preserves local color distributions. We demonstrate intrinsic image decomposition on a variety of images and show applications.
机译:对于许多计算摄影应用,场景中的照明和材料是关键的信息。我们寻求获得内在图像,该内在图像将照片分解为照明组件的乘积,其表示照明效果和反射组件,其是观察材料的颜色。这是一个受限制的问题,自动方法受到复杂自然图像的挑战。我们描述了一种新的方法,使用户能够通过简单的指示来指导优化,例如恒定反射率或照明的区域。基于局部反射率分布的简单假设,我们推出了一种新的传播能量,它可以使用线性最小二乘来实现闭合的形式解决方案。我们通过引入一种保留局部颜色分布的小说下采样来实现快速的性能。我们展示了各种图像上的内在图像分解并显示了应用程序。

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