Single and Multiple Illuminant Estimation Using Convolutional Neural Networks
This addresses color accuracy in imaging for applications like photography and computer vision, but appears incremental as it builds on existing CNN approaches.
The paper tackles the problem of estimating illuminant color in RAW images, both single and multiple, using a specially designed Convolutional Neural Network that produces local estimates and aggregates them based on a detector, achieving lower estimation errors compared to state-of-the-art general purpose methods.
In this paper we present a method for the estimation of the color of the illuminant in RAW images. The method includes a Convolutional Neural Network that has been specially designed to produce multiple local estimates. A multiple illuminant detector determines whether or not the local outputs of the network must be aggregated into a single estimate. We evaluated our method on standard datasets with single and multiple illuminants, obtaining lower estimation errors with respect to those obtained by other general purpose methods in the state of the art.