Separation of reflected images using WFLD

Abstract

Taking photos of objects behind glass always troubles people due to the problem of reflection. This kind of photos are called reflected images. They are composed by two layers, a transmission layer which contains the real image of objects behind glass and a reflection layer which contains the virtual image of objects in front of glass. Therefore, we are interested in separating the two layers. In this thesis, we propose a new approach to solve the problem of separation of reflected images by using Whitened Fisher¿s Linear Discriminant (WFLD) Model. We suppose that the two layers that we would like to separate from the reflected image are from two different classes and we have a training data set which contains training data samples of the two classes. Then, we can form a whitened space of the training data set as suggested in the WFLD theory because the whitened space has certain nice mathematical properties. With these properties, the reflected image can be separated in the whitened space. Finally, the separated two layers in whitened space are projected back into the original image space to get the final separation results. Experiment results show that this method can solve the problem quite well as long as our training data samples are representative enough to their respective classes. Furthermore, they show superior performance compared to the method proposed in [Levin and Weiss 2007].