Film Colorization, Using Artificial Neural Networks and Laws Filters

In this study a new artificial neural network based approach to automatic or semi-automatic colorization of black and white film footages is introduced. Different features of black and white images are tried as the input of a MLP neural network which has been trained to colorize the movie using its first frame as the ground truth. Amongst the features tried, e.g. position, relaxed position, luminance, and so on, we are most interested on the texture features namely the Laws filter responses, and what their performance would be in the process of colorization. Also, the network parameter optimization, the effects of color reduction, and relaxed x-y position of pixels as the feature, are investigated in this study. The results are promising and show that the combination of MLP and texture features is effective in this application.