Transform Domain Methods for Single Image Super-Resolution

Super-resolution of a single image is a highly ill-posed problem since the number of high resolution pixels to be be estimated far exceeds the number of low resolution pixels available. Therefore, appropriate regularization or priors play an important role in the quality of results. In this line of work, we propose a family of methods for learning transform domain priors for the single-image super-resolution problem. Our algorithms are able to better synthesize high frequency textural details as compared to the state-of-the-art.