A study on fast learning-based super-resolution utilizing TV regularization for HDTV
Y. Kawamoto, Satoshi Suzuki, Y. Sakuta, Tomio Goto, Masaru Sakurai · 2012
In this paper, we propose a fast learning-based super-resolution image reconstruction utilizing the Total Variation (TV) regularization method by eliminating redundancy of the reference database. We have achieved 114 times faster computational time compared with that of an ordinary learning-based method. It has been generally considered that the learning-based approach is difficult to be applied to the motion pictures because of its large computational time. We have implemented our system on the CELL processor, and studied a feasibility of applying our system to HDTV receivers. The computational speed we have obtained on the CELL processor is 202 times faster than that of the standard PC. This result indicates a possibility of applying our learning-based super-resolution system to HDTV receivers.