The task of single image super-resolution (SISR) is a highly inverse problem because it is very challenging to reconstruct rich details from blurry images. Most previous super-resolution (SR) methods based on Convolution Neural Network (CNN) tend to design a more complex network structure to directly learn the mapping between low-resolution images and high-resolution images. Nevertheless, it is not the best choice for blindly increasing the depth of the network since the performance improvement may not increase but the computing cost. In order to tackle this problem, we propose an effective method, which integrates the image prior to the model to enhance image reconstruction. In fact, the role of categorical prior as an important image feature has been widespreadly used in several high challenge computer vision tasks. In this work, we propose a region attention network (RAN) to recovery clear SR images with the assistance of categorical prior. The proposed RAN can be divided into two branches: the rough image reconstruction branch and the subtle image reconstruction branch. Meanwhile, we also propose a coupling group to make full use of the feature of two branches. Extensive experiments demonstrate that our RAN obtains satisfactory performance with the help of image categorical prior.

In this paper, a new non-linear distortion robust fingerprint feature named Minutiae Phase Difference Feature (MPDF) is proposed, and used to measure the similarity of fingerprint local structures. Then a novel minutiae expanding method based on Moving Least Squares (MLS) transformation is introduced to regain the lost matched minutiae pairs after obtaining a few valid pairs. Finally a score evaluation strategy based on convex hull of transformed minutiae is adopted, which is more reasonable for partial overlapping fingerprint. Experiments of proposed algorithm conducted on FVC2004 database, comparing with other matching methods, show that the MPDF feature and proposed algorithm have promising performances.

In this paper, a novel method of fingerprint minutiae extraction on grey-scale images is proposed based on the Gabor phase field. The novelty of our approach is that the proposed algorithm is performed on the transform domain, i.e. the Gabor phase field of the fingerprint image. This is different from most existing minutiae extraction methods, in which the minutiae are usually extracted from the binarized and thinned fingerprint image. Experimental results on benchmark data sets demonstrate that the proposed algorithm has promising performances.