To address the problem of edge blurring in traditional space domain fusion of multi-focus images, a multi-focus image fusion method based on Guided Filtering (GF) and difference image was proposed. Firstly, the source images were filtered by GF in different levels, and the difference was performed to the filtered images, so as to obtain the focused feature map. Secondly, the Energy of Gradient (EOG) of the focused feature map was used to obtain initial decision map. And to remove the noisy pixels caused by similar HOG, the spatial consistency verification and morphological operation were performed to initial decision map. Thirdly, to avoid sudden change of image feature, the initial decision map was optimized by GF. Finally, weighted fusion was performed to source images based on the optimized decision map, so as to obtain the fusion image. Three sets of classic multi-focus images were selected as experimental images, and the results obtained by the proposed method and other 9 multi-focus image fusion methods were compared. The subjective visual effects showed that the proposed method was able to better preserve the detailed information of multi-focus images, and four objective evaluation indicators of images processed by the proposed method were significantly better than those of the images processed by comparison methods. Experimental results show that the proposed method can achieve high-quality fusion image, well preserve information in source images, effectively solve edge blurring problem of traditional multi-focus image fusion.

The role of common knowledge of pricing game in electronic marketplaces was studied. By simply changing the demand function and allocation function, seller Agents can acquire the common knowledge about the constantly changing market, rather than inferred individual knowledge. Simulation results indicated that seller Agents tends to be more coordinated in their pricing behaviour and became more intelligent in concerning the problem of whether to cooperate or compete in a long term. Results also shows that common knowledge can improve market effectiveness.