Adaptable K-nearest neighbor for image interpolation

A variant of the k-nearest neighbor algorithm is proposed for image interpolation. Instead of using a static volume or static k, the proposed algorithm determines a dynamic k that is small for inputs whose neighbors are very similar and large for inputs whose neighbors are dissimilar. Then, based on the neighbors that the adaptable k provides and their corresponding similarity measures, a weighted MMSE solution defines filters specific to intrinsic content of a low-resolution input image patch without yielding to the limitations of a non-uniformly distributed training set. Finally, global optimization through a single pass Markovian-like network further imposes on filter weights. The approach is justified by a sufficient quantity of relevant training pairs per test input and compared to current state of the art nearest neighbor interpolation techniques.