Single Image Super-Resolution Via A Progressive Mixture Model

In this paper, a progressive mixture model (PMM) for single image super-resolution is proposed. Our model consists of an offline training stage and an online reconstruction stage, and both stages are conducted progressively by exploiting a uniform iterative scheme. In the training stage, the training dataset is clustered into finer and finer groups, and a set of mixture models are sequentially learned. In the reconstruction stage, residuals of the low-resolution (LR) input image are estimated by using the trained mixture models at increasing levels, and then they are progressively added to the LR image for producing the high-resolution (HR) image. Extensive experimental simulation results have clearly shown that the proposed model consistently delivers highly accurate and visually pleasant HR images, compared to that of the state-of the-art image super-resolution methods.