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 de nes lters speci c to intrinsic content of a lowresolution 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 lter weights. The approach is justi ed by a suf cient quantity of relevant training pairs per test input and compared to current state of the art nearest neighbor interpolation techniques.

• Type: Conference
• Year: 2008
• Where: ICASSP
• Authors: Kenta S. Ni, Truong Q. Nguyen
• Keywords: ICASSP 2008, Image Interpolation, K-nearest Neighbor Algorithm, Nearest Neighbor Interpolation, Signal Processing