4 research outputs found
Context-Patch Face Hallucination Based on Thresholding Locality-Constrained Representation and Reproducing Learning
Face hallucination is a technique that reconstruct high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of image patch. In addition, when they are confronted with misalignment or the Small Sample Size (SSS) problem, the hallucination performance is very poor. To this end, this study incorporates the contextual information of image patch and proposes a powerful and efficient context-patch based face hallucination approach, namely Thresholding Locality-constrained Representation and Reproducing learning (TLcR-RL). Under the context-patch based framework, we advance a thresholding based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulates the case that the HR version of the input LR face is present in the training set, thus iteratively enhancing the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. Additionally, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real-world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL
Recommended from our members
Community detection method based on mixed-norm sparse subspace clustering
Community or group is an important structure in disciplines such as social networks, biology gene expression, and physics systems. Community detections for different types of networks have attracted considerable interest. However, it is still challenging to find meaningful community structures in various networks. In particular, accurate community description and implementation of effective detection algorithms with huge datasets are still not solved. In this paper, we present a novel community detection algorithm based on the theory of sparse subspace clustering (SSC) with mixed-norm constraints. Inspired by the sparse representation of subspace, each community in a given network can span a subspace in some similarity measure space. If the basis of subspaces can be solved, all of the nodes can be represented as a linear combination of the nodes that span the same subspace. By introducing a novel mixed-norm constraint in SCC, the connections of nodes among different communities are modeled as noise to improve the clustering accuracy. The formulation of the basis of subspaces is derived from the self-representation property of data by using SSC. Then, the alternating directions method of multipliers (ADMM) framework is used to solve the formulation. Finally, communities are detected by subspace clustering method. The proposed method is compared with state-of-the-art algorithms on synthetic networks and real-world networks. The experimental results show the effectiveness of the proposed algorithm in accurately describing the community. The results also show that the mixed-norm SSC is a practical approach for detecting communities in huge datasets