Discrete feature transform for low-complexity single-image super-resolution

Dictionary-based super-resolution is actively studied with successful achievements. However, previous dictionary-based super-resolution methods exploit optimization or nearest neighbor search which has high complexity. In this paper, we propose a low-complexity super-resolution method called the discrete feature transform which performs feature extraction and nearest neighbor search at once. As a result, the proposed method achieves the lowest complexity among dictionary-based super-resolution methods with a comparable performance.