This paper proposes an algorithm for exemplar-based image inpainting, which produces the same result as that of Criminisi's original scheme but at the cost of much smaller computation cost. The idea is to compute mean and standard deviation of every patch in the image, and use the values to decide whether to carry out pixel by pixel comparison or not when searching for the best matching patch. Due to the missing pixels in the target patch, the same pixels in the candidate patch should be omitted when computing the distance between patches. Thus, we first compute the range of mean and standard deviation of a candidate patch with missing pixels, using the average and standard deviation of the entire patch. Then we use the range to determine if the pixel comparison should be conducted. Measurements with well-known images in the inpainting literature show that the algorithm can save significant amount of computation cost, without risking degradation of image quality.

For large-scale sensor networks, multiple sinks are often deployed in order to reduce source-to-sink distance and thus cost of data delivery. However, having multiple sinks may work against cost reduction, because routes from sources can diverge towards different sinks which reduces the benefit of in-network data aggregation. In this letter we propose a self-clustering data aggregation protocol (SCAP) that can benefit from having multiple sinks as well as joint routes. In SCAP, nodes which detect the event communicate with each other to aggregate data between themselves, before sending the data to the sinks. The self-clustering extends network lifetime by reducing energy consumption of nodes near the sinks, because the number of paths in which the packets are delivered is reduced. A performance comparison with existing protocols L-PEDAP and LEO shows that SCAP can conserve energy and extend network lifetime significantly, in a multi-sink environment.

This letter presents a scheme to improve the running time of exemplar-based image inpainting, first proposed by Criminisi et al. In the exemplar-based image inpainting, a patch that contains unknown pixels is compared to all the patches in the known region in order to find the best match. This is very time-consuming and hinders the practicality of Criminisi's method to be used in real time. We show that a simple bounding algorithm can significantly reduce number of distance calculations, and thus the running time. Performance of the bounding algorithm is affected by the order of patches that are compared, as well as the order of pixels in a patch. We present pixel and patch ordering schemes that improve the performance of bounding algorithms. Experiments with well-known images used in inpainting literature show that the proposed reordering scheme can reduce running time of the bounding algorithm up to 50%.