Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization

As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications. The success of sparse representation owes to the development of l1-norm optimization techniques, and the fact that natural images are intrinsically sparse in some domain. The image restoration quality largely depends on whether the employed sparse domain can represent well the underlying image. Considering that the contents can vary significantly across different images or different patches in a single image, we propose to learn various sets of bases from a pre-collected dataset of example image patches, and then for a given patch to be processed, one set of bases are adaptively selected to characterize the local sparse domain. We further introduce two adaptive regularization terms into the sparse representation framework. First, a set of autoregressive (AR) models are learned from the dataset of example image patches. The best fitted AR models to a given patch are adaptively selected to regularize the image local structures. Second, the image non-local self-similarity is introduced as another regularization term. In addition, the sparsity regularization parameter is adaptively estimated for better image restoration performance. Extensive experiments on image deblurring and super-resolution validate that by using adaptive sparse domain selection and adaptive regularization, the proposed method achieves much better results than many state-of-the-art algorithms in terms of both PSNR and visual perception.Comment: 35 pages. This paper is under review in IEEE TI

Effect of calcium aluminate cement variety on the hydration of Portland cement in blended system

Two kinds of CACs with different monocalcium aluminate (CA) contents were used in the PC/CAC (PAC) mixtures. Effects of CA and CACs on the properties of PAC were analyzed by setting times and the compressive strength tests, and also by means of calorimetry, XRD, DTA-TG and ESEM. The experimental results show that the compressive strength of the PAC mortars decreases with increasing content of CAC while it declines sharply with a higher content of CA in CAC. Compared with neat PC paste, the content of calcium hydroxide in hydrates of PAC paste decreases significantly, and the hydration time of PC is prominently prolonged. Additionally, the higher the content of CA in CAC, the more obviously the hydration of PC is delayed, confirming that the CA phase in CAC plays an important role in the delay of PC hydration

Linking stoichiometric homeostasis with ecosystem structure, functioning, and stability

Ecosystem structure, functioning, and stability have been a focus of ecological and environmental sciences during the past two decades. The mechanisms underlying their relationship, however, are not well understood. Based on comprehensive studies in Inner Mongolia grassland, here we show that species-level stoichiometric homeostasis was consistently positively correlated with dominance and stability on both 2-year and 27-year temporal scales and across a 1200-km spatial transect. At the community level, stoichiometric homeostasis was also positively correlated with ecosystem function and stability in most cases. Thus, homeostatic species tend to have high and stable biomass; and ecosystems dominated by more homeostatic species have higher productivity and greater stability. By modulating organism responses to key environmental drivers, stoichiometric homeostasis appears to be a major mechanism responsible for the structure, functioning, and stability of grassland ecosystems