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.

• A. Singh and N. Ahuja, Super-Resolution Using Sub-band Self-Similarity, Asian Conference on Computer Vision (ACCV), Singapore, 2014.
• A. Singh and N. Ahuja, Sub-band Energy Constraints for Self-Similarity Based Super-Resolution, International Conference on Pattern Recognition (ICPR), Stockholm, Sweden, August 2014.
• A. Singh and N. Ahuja, Single Image Super-Resolution using Adaptive Domain Transformation, Proceedings of the IEEE International Conference on Image Processing, Melbourne, Australia, Sept. 17, 2013.