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Abstract A dual‐frequency (DF) dielectric barrier discharge, excited by the superposition of a 50 Hz low frequency (LF) and a 5000 Hz intermediate frequency (IF), is proposed to enhance discharge. The effect of the LF voltage component on the breakdown behaviour of the DF discharge during different periods has been studied both experimentally and numerically. The number of high‐current pulses rises as the LF voltage increases. The statistical analysis shows that the number of the current pulse amplitude above 80 mA in the DF discharge reaches nearly 6 times that in the IF discharge. Additionally, the total discharge energy in the DF discharge is significantly higher than that in the IF discharge. The simulation reproduces the temporal variation of the breakdown behaviours in the DF discharge. The simulated results reveal that the maximal electric field strength of the breakdown process is greater in the DF discharge compared to the IF discharge during a half‐period of DF. Finally, the comparison between the IF and DF discharges exhibits that the LF voltage regulates the accumulation of residual charged species on the dielectric surface after the breakdown by modulating the residual voltage between the air gap.

Abstract Inverse tone mapping technique is widely used to restore the lost textures from a single low dynamic range image. Recently, many stack‐based deep inverse tone mapping networks have achieved impressive results by estimating a set of multi‐exposure images from a single low dynamic range input. However, there are still some limitations. On the one hand, these methods usually set a fixed length for the estimated multi‐exposure stack, which may introduce computational redundancy or cause inaccurate results. On the other hand, they neglect that the difficulties of estimating each exposure value are different and use the identical model to increase or decrease exposure value. To solve these problems, the authors design an exposure decision network to adaptively determine the number of times the exposure of low dynamic range input should be increased or decreased. Meanwhile, the authors decouple the increasing/decreasing process into two sub‐modules, exposure adjustment and optional detail recovery, based on the characteristics of different variations of exposure values. With these improvements, this method can fast and flexibly estimate the multi‐exposure stack from a single low dynamic range image. Experiments on several datasets demonstrate the advantages of the proposed method compared to state‐of‐the‐art inverse tone mapping methods.