IEEE Trans Image Process. 2017 Sep;26(9):4114-4127. doi: 10.1109/TIP.2017.2712283. Epub 2017 Jun 6.
Illumination decomposition for a single photograph is an important and challenging problem in image editing operation. In this paper, we present a novel coarse-to-fine strategy to perform illumination decomposition for photograph with multiple light sources. We first reconstruct the lighting environment of the image using the estimated geometry structure of the scene. With the position of lights, we detect the shadow regions as well as the highlights in the projected image for each light. Then, using the illumination cues from shadows, we estimate the coarse illumination decomposed image emitted by each light source. Finally, we present a light-aware illumination optimization model, which efficiently produces the finer illumination decomposition results, as well as recover the texture detail under the shadow. We validate our approach on a number of examples, and our method effectively decomposes the input image into multiple components corresponding to different light sources.
单张照片的光照分解是图像编辑操作中的一个重要且具有挑战性的问题。在本文中，我们提出了一种新颖的从粗到精的策略，用于对具有多个光源的照片进行光照分解。我们首先使用场景估计的几何结构重建图像的光照环境。根据灯光的位置，我们检测投影图像中的阴影区域和每个灯光的高光区域。然后，我们利用阴影中的光照线索，估计每个光源发出的粗略光照分解图像。最后，我们提出了一种光感知光照优化模型，该模型能够有效地产生更精细的光照分解结果，并恢复阴影下的纹理细节。我们在多个示例上验证了我们的方法，我们的方法可以有效地将输入图像分解为对应于不同光源的多个分量。
