A new image super resolution by texture transfer

This paper proposes the new super resolution algorithm for improving the texture of low-quality image. Conventional methods have restored the high-resolution image based on the linear blur model with a variety of priors, which boost the frequency powers of the images while preventing from being noisy or having jagged edges. Previous methods show good performance for restoring the sharp and jagged-free edges. However, in the point of texture, they often fail to restore and result in cartoon-like images. To solve this problem, proposed method focuses on the property of natural textures and tries to create the fine texture from self-image by texture transfer, which modulates the phase of the high-frequency components of image in spatial and frequency domains. The sign of DCT coefficients are modulated to change the phase of image in frequency domain, and the high-frequency components of image are reshaped with an auto regressive filter in spatial domain. Moreover, the external noise sources are added to enhance the pixel-precision textures. Experimental results show that the texture of low-quality images is highly improved than that of previous methods. The fine textures created by proposed method give more natural feeling of highresolution image than that of results restored without a consideration of restoring textures.