Image resolution enhancement via sparse interpolation on wavelet domain

The image processing algorithms collectively known as super-resolution (SR) have proven effective in producing high-quality imagery from low-resolution (LR) images. This paper focuses on a novel image resolution enhancement method employing the wavelet domain techniques. In order to preserve more edge information, additional edge extraction step is proposed employing high-frequency (HF) sub-band images - low-high (LH), high-low (HL), and high-high (HH) - via the Discrete Wavelet Transform (DWT). In the designed procedure, the LR image is used in the sparse interpolation for the resolution-enhancement obtaining low-low (LL) sub- band. Additionally, all sub-bands (LL, LH, HL and HH) are performed via the Lanczos interpolation. Finally, the estimated sub-band images are used to form the new high-resolution (HR) image using the inverse DWT (IDWT). Experimental results on real data sets have confirmed the effectiveness of the proposed framework in terms of objective criteria as well as in subjective perception.