We propose a novel intrinsic image decomposition network considering reflectance consistency. Intrinsic image decomposition aims to decompose an image into illumination-invariant and illumination-variant components, referred to as ``reflectance'' and ``shading,'' respectively. Although there are three consistencies that the reflectance and shading should satisfy, most conventional work does not sufficiently account for consistency with respect to reflectance, owing to the use of a white-illuminant decomposition model and the lack of training images capturing the same objects under various illumination-brightness and -color conditions. For this reason, the three consistencies are considered in the proposed network by using a color-illuminant model and training the network with losses calculated from images taken under various illumination conditions. In addition, the proposed network can be trained in a self-supervised manner because various illumination conditions can easily be simulated. Experimental results show that our network can decompose images into reflectance and shading components.
翻译:我们提出一个新的内在图像分解网络,考虑反射的一致性。内在图像分解旨在将图像分解成分别称为“反射”和“阴影”的光化-异变和光化-异变组成部分。虽然反射和阴影应该满足三个方面,但大多数常规工作没有充分考虑到与反射的一致性,因为使用白光分解模型和缺乏在各种光化-光化-光化和-彩色条件下捕捉相同物体的培训图像。因此,在拟议的网络中,通过使用彩色照明模型和对网络进行培训,从不同照明条件下拍摄的图像中计算损失。此外,拟议的网络可以自我监督地培训,因为各种污染条件很容易被模拟。实验结果显示,我们的网络可以将图像分解成反射和阴影组成部分。