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In this study, 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...
In this study, video super-resolution using artificial neural network (ANN) is proposed to enlarge low-resolution (LR) frames. The proposed super-resolution method consists of three main modules, i.e., motion-trace volume collection, ANN training, and ANN prediction. In the proposed method, the LR frames are super-resolved to HR frames through ANN. The traditional motion estimation is used to catch...
An improved motion-search method based on pattern classification is proposed in this paper. A new feature representing the maximum motion around the current block is introduced, allowing more precise description of the motion characteristics of the block. Ada-Boost classifiers can correctly classify the harder-to-classify samples by increasing the weights of misclassified samples, so are adopted to...
In this study, a new video super-resolution (SR) reconstruction approach using a mobile search strategy and adaptive patch size is proposed. Based on the nonlocal-means (NLM) SR algorithm, the mobile strategy for search center and adaptive patch size are proposed to reduce the computational complexity and improve the visual quality, respectively. Based on the experimental results obtained in this...
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