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Contrast enhancement is a crucial image processing step for image quality control. However, images enhanced by conventional contrast enhancement methods can have negative effects on the performance of image quality control. The most commonly observed effects are over- and under-enhancement effects on images, which cause significant loss of fine textures in images. This paper developed a new contrast enhancement algorithm based on an entropy-preserving mapping prior that improves on conventional contrast enhancement methods. By creating a closed-form solution for enhancing the image contrast under this novel prior and learning the coefficients of the solution using an unsupervised learning strategy, an image’s contrast and texture can be effectively recovered. The experimental results verify that our proposed method clearly outperforms the existing state-of-the-art methods in terms of both quantitative estimation and qualitative human visual inspection. [ABSTRACT FROM AUTHOR]

To efficiently perform multiscale analysis of high-resolution, global, multiple-dimensional datasets, the authors have deployed the parallel ensemble empirical mode decomposition (PEEMD) package by implementing three-level parallelism into the ensemble empirical mode decomposition (EMD), achieving a scaled performance of 5,000 cores. In this study, they discuss the implementation of the PEEMD and its application for the analysis of Earth science data, including the solution of the Lorenz model, an idealized terrain-induced flow, and Hurricane Sandy. [ABSTRACT FROM PUBLISHER]