The Human Visual System allows us to perceive the world around us. Illumination has a significant effect on an object's color, but humans perceive object color as relatively constant regardless of illumination color. This ability is called color constancy. Unlike humans, cameras are unable to do this automatically, resulting in unnatural-looking images. Methods that emulate the Human Visual System have been proposed. Research has shown that learning-based methods achieve the best results. For the proper usage of learning-based methods, a large number of samples are needed. There are not many multi-illuminant datasets, as their creation is a laborious process. This thesis explores both illumination estimation method development as well as methods for the creation of multi-illuminant datasets. For illumination estimation, five methods, one for single-illuminant and four for multi-illuminant estimation, were developed. The single-illuminant method is a lightweight convolutional neural network that achieves state-of-the-art results on existing datasets. The multi-illuminant methods were created for three situations: one method for when the number of illuminants is known a priori, one method for patch-by-patch estimation, and two methods for pixel-by-pixel estimation. Two dataset creation methods were also developed: one for the creation and labeling of real-world two-illuminant images and one for the creation of images with diverse non-uniform illumination. Experiments show that all proposed methods achieve comparable or better results than methods from the literature.