Fast image replacement using multi-resolution approach

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 the training process by using this image data. According to the training non-periodic or periodic pattern, we use different sampling methods to have different amount of patches in order to reduce the emergences of the seams of the output synthesized image. In addition, 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 (i.e. eigenvectors). Then the vector quantization (VQ) algorithm is employed to reduce the time spent on matching comparison. For the synthesis module, the training data is used to synthesize a large output texture, or is employed to replace the removed regions of an image. The multi-resolution approach is applied to accelerate 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 large removed region without needing to assign initial random values or approximate values. Therefore, our system can rapidly obtain a high image quality and promising result.