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To obtain ideal repair effects, an improved Criminisi algorithm-based image repair algorithm was proposed specific to the shortcomings of Criminisi algorithm which costs much repairing time, its feasibility and superiority were tested. First, priority calculation was improved to find optimal to-be-repaired block; then optimal matching block search strategy was improved to find optimal matching block; finally, new confidence update modes were adopted to obtain more ideal repair effects and the simulation experiment was made to test the algorithm performance. Results indicated that compared with Criminisi algorithm not only could obtain ideal image repair effects but also could sharply reduce repair time and improve image repair efficiency.

In view of the stability and reliability of energy supply, distinct from the time‐varying and uncertainty of energy harvesting systems, adopting mobile vehicles to replenish energy of sensors has become a research hotspot. While some existing studies on the mobile recharging problem ignored the limited energy capacity carried by mobile vehicle and the difference in energy consumption rates of sensors, in this work, we propose an adaptive real‐time on‐demand charging scheduling scheme that maximizes energy efficiency (CSS‐MEE) for Rechargeable Wireless Sensor Networks. In CSS‐MEE, we aim to achieve a compromise between maximizing charging energy efficiency and maximizing charging throughput for solving the on‐demand mobile charging problem. Due to the limited energy capacity of the mobile charger, CSS‐MEE uses both full‐charge mode and adaptive charging mode, depending on the number of charging requests. It combines charging node selection with dispatch path feasibility determination, which takes into account the location‐generated charging cost and energy‐driven charging priority, to ensure the charging efficiency. Extensive simulations are conducted to demonstrate the advantages of CSS‐MEE. Compared with existing approaches, simulation results show that CSS‐MEE achieves better performance in terms of charging throughput, average charging latency, charge scheduling times, and charging efficiency.

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