  An image super-resolution method from multiple observation of low-resolution
images is proposed. The method is based on sub-pixel accuracy block matching
for estimating relative displacements of observed images, and sparse signal
representation for estimating the corresponding high-resolution image. Relative
displacements of small patches of observed low-resolution images are accurately
estimated by a computationally efficient block matching method. Since the
estimated displacements are also regarded as a warping component of image
degradation process, the matching results are directly utilized to generate
low-resolution dictionary for sparse image representation. The matching scores
of the block matching are used to select a subset of low-resolution patches for
reconstructing a high-resolution patch, that is, an adaptive selection of
informative low-resolution images is realized. When there is only one
low-resolution image, the proposed method works as a single-frame
super-resolution method. The proposed method is shown to perform comparable or
superior to conventional single- and multi-frame super-resolution methods
through experiments using various real-world datasets.
