A lightweight stacked residual low-light image enhancement network for USV optical vision perception
A lightweight stacked residual low-light image en-hancement network was proposed to overcome the difficulty in accurately sensing the environment under low-light conditions for unmanned surface vessels(USV).Firstly,a pyramid multi-scale pooling was introduced into feature fusion to better preserve image details.Secondly,depthwise separable convo-lution was introduced to reduce network burden and improve the image processing speed.Thirdly,a new composite loss function with color loss was designed to reduce color distor-tion.Finally,LeakyReLU activation function was used to pre-vent neuronal death.Results show that compared to the low il-lumination image enhancement network of stacked attention residual network(SARN),the proposed method improves im-age quality while accelerating image processing speed.The structural similarity and peak signal-to-noise ratio are im-proved by 3.31％and 2.08％,respectively.The model com-putation,parameter count,and single image processing time are reduced by 81.88％,75％,and 43.02％,respectively.
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