Multimodality image fusion provides more comprehensive information and has an increasingly wide range of uses. For the remote sensing image fusion, traditional multiresolution analysis (MRA)-based methods always have insufficiencies in contrast with spatial details. At the same time, traditional sum of modified Laplace may do blocking artifacts. In order to overcome these deficiencies, we propose a remote sensing image fusion method based on the mutual-structure for joint filtering and saliency detection. Our method uses joint filtering to facilitate the correct extraction of the high and low frequency from source images. The saliency detection method also improves the effect of low-frequency fusion, and the high-frequency sub-bands calculate the extended sum of modified Laplace for better fusion. The method is compared with other five classical fusion methods. The experimental results show that the algorithm effectively preserves the structural information and textural information of the image and improves the sharpness of the fused image. It turns out to have many advantages in subjective and objective evaluation.
In the case of poor lighting conditions, it is easy to capture the underexposed images with low contrast and low quality. Traditional single-image enhancement methods often fail in revealing image details because of the limited information in a single-source image. A single underexposed image enhancement method based on adaptive decomposition and convolutional neural network (CNN) is proposed. The CNN training models only need images with different brightness rather than a strict ground-truth image. First, a simple effective synchronous decomposition method is proposed to solve the synergy problem in multisource image decomposition. Then, two CNN models are designed for the high-frequency part and low-frequency part, respectively. They process the high-frequency and low-frequency sub-bands, instead of the entire source images. The weight map obtained from the CNN model represents the contrast distribution. The exposure map generated by gradient-based visibility assessment indicates the exposure distribution. Finally, the weight map and the exposure map are multiplied to generate the final decision map. Experimental results demonstrate that the proposed method outperforms competing methods.
XML data is widely used in the information exchange field of Internet, and XML document data clustering is the hot research topic. In the XML document clustering process, measure differences between two XML documents is time costly, and impact the efficiency of XML document clustering. This paper proposed an XML documents clustering method based on frequent patterns of XML document dataset, first proposed a coding tree structure for encoding the XML document, and translate frequent pattern mining from XML documents into frequent pattern mining from string. Further, using the cosine similarity calculation method and cohesive hierarchical clustering method for XML document dataset by frequent patterns. Because of frequent patterns are subsets of the original XML document data, so the time consumption of XML document similarity measure is reduced. The experiment runs on synthetic dataset and the real datasets, the experimental result shows that our method is efficient.