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论文类型：期刊论文

发表时间：2019-02-01

发表刊物：SIGNAL PROCESSING-IMAGE COMMUNICATION

收录刊物：SCIE、Scopus

卷号：70

页面范围：210-219

ISSN号：0923-5965

关键字：Super-resolution; Self-similarity; Deep learning; Spatial transformer networks

摘要：A single image super-resolution (SR) algorithm that combines deep convolutional neural networks (CNNs) with multi-scale similarity is presented in this work. The aim of this method is to address the incapability of the existing CNN methods in digging the potential information in the image itself. In order to dig these information, the image patches that look similar within the same scale and across the different scales are firstly searched inside the input image. Subsequently, a spatial transform networks (STNs) are embedded into the CNNs to make the similar patches well aligned. The STNs allow the CNNs to have the ability of spatial manipulation of data. Finally, when SR is performing through the proposed pyramid-shaped CNNs, the high-resolution (FIR) image will be predicted gradually according to the complementary information provided by these aligned patches. The experimental results confirm the effectiveness of the proposed method and demonstrate it can be compared with state-of-the-art approaches for single image SR.