Improving example-based super-resolution via clustering training sets
Qinlan Xie, Hong Chen · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
More image patches in a training set making it more time-consuming has become a holdback of the real-time application of example-based super-resolution. The paper proposes a method which clusters these training set to accelerate the procedure. Before the super-resolution, a clustering method is used to partition the middle-frequency components in the training set into some subsets. During super-resolution, the distances between each matching patch of low-resolution image and each subset of training set are computed. The subset with the minimum distance is selected to carry out farther matching. This procedure goes along until a most matching patch is found. The high-frequency patch within the training set relevant to the found matching patch is selected as the researching output, which is used for super-resolution of objective image. Two examples are use to illustrate the performance of the proposed algorithm, one using a factitious image obtained by blurring and down-sampling an original image, and another using directly a true image. The results show the proposed method can reduce effectively the computational complexity.