Multiple-content Image Colorization Algorithm Based On Multiple Dictionary

Image colorization which belongs to the research in the field of image restoration is a hot issue in digital image processing area. Colorization is a kind of computer processing of adding colors to old grayscale film、television or image. On the one hand, due to the poor equipment and conditions, most of the early photographs or films have no color. On the other hand, the formation of some grayscale images is related to the special imaging mechanism. Compared to the grayscale images, color images have more prominent details, more realistic content and visual effects. Therefore, colorization technique is worth studying and it has important application value. So far, colorization technique has been widely used in movie processing, medical instrument, space exploration and other fields.The state-of-the-art colorization methods can mainly be categorized into two groups. The first group is scribble-based colorization. The second group is example-based colorization. The thesis mainly focuses on method of second group. With the development of compressed sensing, sparse representation and dictionary learning attracted more and more attention. After the study of many related scholars, it has gradually formed a set of independent theoretical system which applied in many fields successfully. The thesis investigates the theory and algorithm of sparse representation and dictionary learning and focus on how to use this effective tool to solve image colorization problem. In order to solve the problems existing in the traditional image colorization algorithm based on single dictionary and sparse representation, we propose two algorithms, Image colorization algorithm based on classification dictionary and sparse representation and Image colorization algorithm based on sparse representation and combined dictionary. The main contribution of the thesis is shown as follows:(1) Image colorization algorithm based on classification dictionary and sparse representation is proposed in the thesis. According to the idea of classification dictionary and the decision criterion of dictionary matching, the different contents of the target grayscale image block are processed by different classification dictionary. In this way, we well solved the traditional problems and implemented the multi-content image colorization.(2) In order to solve the time consuming problem in the process of dictionary matching, Image colorization algorithm based on sparse representation and combined dictionary is proposed, which improve the core part of image colorization algorithm based on classification dictionary. The new algorithm has lower time complexity、higher matching accuracy and further enhance the color effect. As a time consuming task, dictionary learning is an important part of the two algorithms proposed in the thesis. In order to improve the colorization algorithm efficiency and reduce the cost of the algorithm, in thesis, we put forward the concept of off-line dictionary library, and apply it to the actual color process.