Content adaptive image superresolution with gradient consistency and anisotropic regularization

In this paper, we propose content adaptive image superresolution (SR) using gradient consistency and anisotropic regularization. The gradient consistency term effectively suppresses visual artifacts such as ringing and preserves sharp edges in images while the anisotropic regularization term adaptively preserves the high frequency information according to the gradient magnitude. The complementary two terms are elaborately combined into the gradient-consistency-anisotropic-regularization (GCAR) prior for the SR reconstruction. The GCAR prior is very effective in preserving image details and recovering high frequency information. Moreover, the proposed SR method employs an effective feedback-control loop which contains content adaptive de-convolution, re-convolution, and pixel substitution, and the GCAR prior is utilized in the content adaptive de-convolution step. Extensive experiments on various test images demonstrate that the proposed method produces natural-looking SR results in terms of both visual quality and quantitative performance.