Different objects in the same scene emit the incident light differently and are perceived differently colored. The human visual system is very robust to changes in the scene and adapts rapidly, and can perceive objects' colors invariant of the light source. Image sensors in digital cameras do not have this ability, and thus a pre-processing step is dedicated to achieving invariance of colors to the scene illumination. That step is referred to as computational color constancy and consists of illumination estimation and chromatic adaptation. The focus of this thesis is on illumination estimation, and the aim is the analysis and implementation of various approaches to illumination estimation using deep learning methodology. Deep neural networks have exceptional generalization capability and achieve state-of-the-art results in computer vision. However, deep learning usually involves the question of large datasets that are not typical in illumination estimation. Therefore, a part of the research in the thesis is dedicated to defining the methodology for creating a dataset for illumination estimation, and a novel dataset is presented.The scientific contribution of the doctoral thesis is divided into four parts: illumination color estimation method based on a convolutional neural network with an attention mechanism; illumination color estimation method using scene lighting classification based on deep learning; illumination color estimation method based on recurrent deep neural network with a multistage loss function; the Cube++ illumination estimation dataset.