Helicopter blade low-light image enhancement is an indispensable preprocessing step for helicopter blade motion parameter measurement. Aiming at the problems of low image contrast, noise interference, and the scarcity of paired low-light/normal-light training data, this paper proposes a zero-shot network-based enhancement method. Firstly, an illumination correction module based on an exposure correction S-curve is constructed, where the illumination component of the image is directly enhanced using the optimal Scurve parameters estimated by the network. Secondly, an adaptive convolutional network is designed to extract and suppress image noise. Finally, a zero-shot-based loss function is designed to enable the adaptive training of the entire network without reliance on paired training sets. Experimental results show that the objective evaluation indicators and visual quality of the proposed method on multiple datasets are superior to the state-of-the-art algorithms. Moreover, the network computing capacity required is only 1.24 G, which meets the needs of real-time enhancement of helicopter blade low-light images under airborne conditions.
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