Figure 1High quality single image motion-deblurring. The left sub-figure shows one captured image using a hand-held camera under dim light. It is severely blurred by an unknown kernel. The right sub-figure shows our deblurred image result computed by estimating both the blur kernel and the unblurred latent image. We show several close-ups of blurred/unblurred image regions for comparison. AbstractWe present a new algorithm for removing motion blur from a single image. Our method computes a deblurred image using a unified probabilistic model of both blur kernel estimation and unblurred image restoration. We present an analysis of the causes of common artifacts found in current deblurring methods, and then introduce several novel terms within this probabilistic model that are inspired by our analysis. These terms include a model of the spatial randomness of noise in the blurred image, as well a new local smoothness prior that reduces ringing artifacts by constraining contrast in the unblurred image wherever the blurred image exhibits low contrast. Finally, we describe an efficient optimization scheme that alternates between blur kernel estimation and unblurred image restoration until convergence. As a result of these steps, we are able to produce high quality deblurred results in low computation time. We are even able to produce results of comparable quality to techniques that require additional input images beyond a single blurry photograph, and to methods that require additional hardware.

Figure 1High quality single image motion-deblurring. The left sub-figure shows one captured image using a hand-held camera under dim light. It is severely blurred by an unknown kernel. The right sub-figure shows our deblurred image result computed by estimating both the blur kernel and the unblurred latent image. We show several close-ups of blurred/unblurred image regions for comparison. AbstractWe present a new algorithm for removing motion blur from a single image. Our method computes a deblurred image using a unified probabilistic model of both blur kernel estimation and unblurred image restoration. We present an analysis of the causes of common artifacts found in current deblurring methods, and then introduce several novel terms within this probabilistic model that are inspired by our analysis. These terms include a model of the spatial randomness of noise in the blurred image, as well a new local smoothness prior that reduces ringing artifacts by constraining contrast in the unblurred image wherever the blurred image exhibits low contrast. Finally, we describe an efficient optimization scheme that alternates between blur kernel estimation and unblurred image restoration until convergence. As a result of these steps, we are able to produce high quality deblurred results in low computation time. We are even able to produce results of comparable quality to techniques that require additional input images beyond a single blurry photograph, and to methods that require additional hardware.

There is a growing public concern over the potential accumulation of heavy metals in agricultural soils in China owing to rapid urban and industrial development and increasing reliance on agrochemicals in the last several decades. Excessive accumulation of heavy metals in agricultural soils may not only result in environmental contamination, but elevated heavy metal uptake by crops may also affect food quality and safety. The present study is aimed at studying heavy metal concentrations of crop, paddy and natural soils in the Pearl River Delta, one of the most developed regions in China. In addition, some selected soil samples were analyzed for chemical partitioning of Co, Cu, Pb and Zn.The Pb isotopic composition of the extracted solutions was also determined. The analytical results indicated that the crop, paddy and natural soils in many sampling sites were enriched with Cd and Pb. Furthermore, heavy metal enrichment was most significant in the crop soils, which might be attributed to the use of agrochemicals.

[1] Simultaneous measurements of atmospheric organic and elemental carbon (OC and EC) were taken during winter and summer seasons at 2003 in 14 cities in China. Daily PM 2.5 samples were analyzed for OC and EC by the Interagency Monitoring of Protected Visual Environments (IMPROVE) thermal/optical reflectance protocol. Average PM 2.5 OC concentrations in the 14 cities were 38.1 mg m À3 and 13.8 mg m À3 for winter and summer periods, and the corresponding EC were 9.9 mg m À3 and 3.6 mg m À3 , respectively. OC and EC concentrations had summer minima and winter maxima in all the cities. Carbonaceous matter (CM), the sum of organic matter (OM = 1.6 Â OC) and EC, contributed 44.2% to PM 2.5 in winter and 38.8% in summer. OC was correlated with EC (R 2 : 0.56-0.99) in winter, but correlation coefficients were lower in summer (R 2 : 0.003-0.90). Using OC/EC enrichment factors, the primary OC, secondary OC and EC accounted for 47.5%, 31.7% and 20.8%, respectively, of total carbon in Chinese urban environments. More than two thirds of China's urban carbon is derived from directly emitted particles. Average OC/EC ratios ranged from 2.0 to 4.7 among 14 cities during winter and from 2.1 to 5.9 during summer. OC/EC ratios in this study were consistent with a possible cooling effect of carbonaceous aerosols over China.