Update of deep network framework to super-resolution reconstruction has been greatly improved, but there are great problems of loss of texture information and decreased image detail quality. In this paper, we have constructed network focus on texture features structure, which can generate SR images by taking full advantage of low resolution images and improve the efficiency of generation. In our method, we first adopt extract detail texture information by kernel diversity network (KDN)which is a combined with residual network to extensive extract various feature of low dimensional images. Particularly, KDN is derived from the processing of the original image and has the ability to prevent information loss and its operation according to certain combination mode by convolution operations with different properties. Furthermore, we design pyramid amplification networks that improving generation speed and image quality to maximizing utilization information of the original image. Our final results show that an SR network with KDN and pyramid networks can generate more natural and clear texture in comparison to state-of-the-art methods.
Single image super-resolution(SR) reconstruction aims to recover the corresponding high resolution(HR) image through one low resolution(LR) image. SR reconstruction is an ill-posed problem, therefore, an effective image prior knowledge is meaningful to reconstruct the missing details in the LR image. In this paper, we propose a SR method by making use of the directional properties of image edges to construct local smoothing prior and non-local similarity prior. We utilize the directionlet that can effectively represent the image edge direction information to extract the directional feature information, after that, these directional information is used in the reconstruction framework based on TV and NLM to better protect the sharp edges of the image and improve the reliability of self-similar weight. The experimental results demonstrate that the proposed algorithm outperforms some of the current SR methods in terms of quantitatively and qualitatively.