Abstract:

Color image super-resolution reconstruction based on the sparse representation model usually adopts the regularization norm (e.g., $L_{1}$ or $L_{2})$. These methods have limited ability to keep image texture detail to some extent and are easy to cause the problem of blurring details and color artifacts in color reconstructed images. This paper presents a color super-resolution reconstruction method combining the $L_{2 / 3}$ sparse regularization model with color channel constraints. The method converts the low-resolution color image from RGB to YCbCr. The $L_{2 / 3}$ sparse regularization model is designed to reconstruct the brightness channel of the input low-resolution color image. Then the color channel-constraint method is adopted to remove artifacts of the reconstructed high-resolution image. The method not only ensures the reconstruction quality of the color image details, but also improves the removal ability of color artifacts. The experimental results on natural images validate that our method has improved both subjective and objective evaluation.

Key words: color image, sparse representation, super-resolution, $L_{2 / 3}$ regularization norm, color channel constraint