Training Database Adequacy Analysis for Learning-Based Super-Resolution

This paper explores the possibility of assessing the adequacy of a training database to be used in a learning-based super-resolution process. The Mean Euclidean Distance (MED) function is obtained by averaging the distance between each input patch and its closest candidate in the training database, for a series of blurring kernels used to construct the low-resolution database. The shape of that function is thought to indicate the level of adequacy of the database, thus indicating to the user the potential of success of a learning-based super-resolution algorithm using this database.