A PCA-Based Super-Resolution Algorithm for Short Image Sequences
This addresses the demanding problem of obtaining high-resolution images from limited data, such as in scanned graphics and text, but it is incremental as it builds on existing two-step super-resolution approaches.
The authors tackled the problem of super-resolution from short image sequences by proposing a two-step PCA-based algorithm that first increases sampling via local principal component models and then restores artifacts with a learned linear filter. Results on a five-image sequence of graphics and text showed excellent performance compared to state-of-the-art methods.
In this paper, we present a novel, learning-based, two-step super-resolution (SR) algorithm well suited to solve the specially demanding problem of obtaining SR estimates from short image sequences. The first step, devoted to increase the sampling rate of the incoming images, is performed by fitting linear combinations of functions generated from principal components (PC) to reproduce locally the sparse projected image data, and using these models to estimate image values at nodes of the high-resolution grid. PCs were obtained from local image patches sampled at sub-pixel level, which were generated in turn from a database of high-resolution images by application of a physically realistic observation model. Continuity between local image models is enforced by minimizing an adequate functional in the space of model coefficients. The second step, dealing with restoration, is performed by a linear filter with coefficients learned to restore residual interpolation artifacts in addition to low-resolution blurring, providing an effective coupling between both steps of the method. Results on a demanding five-image scanned sequence of graphics and text are presented, showing the excellent performance of the proposed method compared to several state-of-the-art two-step and Bayesian Maximum a Posteriori SR algorithms.