Fast Image Replacement through Texture Synthesis

We developed a system including two modules: the texture analysis module and the texture synthesis module. The analysis module is capable of analyzing an input image and performing training using this image data. The properties of principal component analysis (PCA) are used to reduce the dimensions of the data representation and to recombine the appearance of the features. Additionally, the vector quantization (VQ) algorithm is employed to reduce the time spent on comparison. For the synthesis module, the training data is used to rapidly synthesize a large output texture, or is employed to rapidly replace the removed regions of an image. The multi-resolution approach is applied to speed up the procedure of our algorithm: the down-sampling step is the training process and the up-sampling step is in the order of reconstructing (or synthesizing) the replaced region. That is, our system can rapidly obtain a high image quality and a promising result.