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The single image super-resolution method can improve blur edges in a magnified image. However, it is difficult to improve the sharpness of a texture region in a magnified image since high frequency components of texture are different from others. In this paper, we propose super-resolution from a single image based on total variation (TV) regularization to improve sharpness of texture in magnified image. We use diverged components of total variation regularization to generate high frequency components. We show experimentally that the diverged components can improved the sharpness of texture in the magnified image. However, the noise as visual distortion occurs except for texture region by using the diverged components. Therefore, we propose a determination method for optimum  that is a TV parameter to generate an appropriate high quality magnified image meaning an image with sharp texture and low noise in the edges and background. Our proposed method can generate sharper texture in a magnified image than Glasner’s method which is representation method of single image super-resolution.

Super-Resolution from a Single Image to Improve Sharpness of Texture Based on Optimum Parameter of Total Variation Regularization