  Illuminant estimation plays a key role in digital camera pipeline system, it
aims at reducing color casting effect due to the influence of non-white
illuminant. Recent researches handle this task by using Convolution Neural
Network (CNN) as a mapping function from input image to a single illumination
vector. However, global mapping approaches are difficult to deal with scenes
under multi-light-sources. In this paper, we proposed a self-adaptive single
and multi-illuminant estimation framework, which includes the following
novelties: (1) Learning local self-adaptive kernels from the entire image for
illuminant estimation with encoder-decoder CNN structure; (2) Providing
confidence measurement for the prediction; (3) Clustering-based iterative
fitting for computing single and multi-illumination vectors. The proposed
global-to-local aggregation is able to predict multi-illuminant regionally by
utilizing global information instead of training in patches, as well as brings
significant improvement for single illuminant estimation. We outperform the
state-of-the-art methods on standard benchmarks with the largest relative
improvement of 16%. In addition, we collect a dataset contains over 13k images
for illuminant estimation and evaluation. The code and dataset is available on
https://github.com/LiamLYJ/KPF_WB
