| Summary: | 博士 === 國立中正大學 === 資訊工程研究所 === 101 === Video super-resolution is an important issue in contemporary applications. Two main reasons prompt the demand for such technology, namely low-resolution (LR) capturing and low bandwidth communication, in which high-resolution (HR) display is required at the user end. In video super-resolution, construction of one HR frame can be performed from a set of successive LR frames, instead of from just one LR frame. The better construction results can be obtained.
Soft computing can be divided into two categories, namely, optimization algorithm and machine learning. Optimization algorithm includes particle swarm optimization (PSO), genetic algorithm (GA), and etc. Machine learning includes artificial neural network (ANN), support vector machine (SVM), and etc. PSO is a population-based algorithm for searching sub-optima. It can be used to solve complicated optimization problems with low cost. Artificial neural network (ANN) is a biologically motivated learning machine inspired from biological neurons and the nervous systems. ANN serves as powerful computational tool for nonlinear prediction problem.
In this thesis, first we propose a super-resolution method which consists of three main modules, i.e., supersampling, spatio-temporal classification, and frame fusion using PSO. In the proposed method, the LR frames are super-resolved to high-resolution frames through the fusion of four full-resolution frames. One of four full-resolution frames is obtained using direct spatial interpolation, and the other three are obtained using motion compensation with given reference frames. The essence of the proposed method is the spatio-temporal classification mechanism that exploits the temporal variation between frames and the spatial energy inside the frame. Using the classification results, PSO is used to determine the optimal weights for frame fusion.
Second, a new video super-resolution approach using a mobile search strategy and adaptive patch size is proposed. Based on the modified nonlocal-means (NLM) super-resolution algorithm, a mobile search strategy for motion estimation and adaptive patch size are proposed to reduce the computational complexity of the proposed approach and improve the visual quality of the final video super-resolution results, respectively.
Finally, a classification-based video super-resolution method using artificial neural network (ANN) is proposed to enhance low-resolution (LR) to high-resolution (HR) frames. The proposed method consists of four main steps: classification, motion-trace volume collection, temporal adjustment, and ANN prediction. A classifier is designed based on the edge properties of a pixel in the LR frame to identify the spatial information. To exploit the spatio-temporal information, a motion-trace volume is collected using motion estimation, which can eliminate unfathomable object motion in the LR frames. In addition, temporal lateral process is employed for volume adjustment to reduce unnecessary temporal features. In the final step, ANN is applied to each class to learn the complicated spatio-temporal relationship between LR and HR frames.
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