Selective frequency decomposition in the wavelet domain for single image-based super-resolution

This paper presents single-image based super-resolution (SR) algorithm using selective frequency decomposition in the wavelet domain. We synthesize the diagonal high-frequency (HH) sub-band of the discrete wavelet transform (DWT) using the low frequency component of the low-resolution (LR) image and the high frequency component of the bi-cubic interpolated image in the HH sub-band of the DWT. The reconstructed HR image is obtained by inverse wavelet transforming the synthesized HH sub-band together with the remaining three sub-bands. The HR image is further enhanced using directional edge sharpening. The proposed single-image based SR algorithm can be used to provide digital zoom or SR functions for consumer imaging devices such as mobile phone cameras, camcorders, and surveillance cameras. Experimental results demonstrate that the proposed algorithm outperforms the existing single-image based SR algorithms in the sense of having reconstructed high-frequency components with minimum ringing artifacts.