Low-light visibility enhancement for improving visual surveillance in intelligent waterborne transportation systems

Under low-light imaging conditions, visual scenes captured by intelligent waterborne transportation systems often suffer from low-intensity illumination and noise corruption. The visual quality degradation would lead to negative effects in maritime surveillance, e.g., vessel detection, positioning and tracking, etc. To restore the low-light images, we develop an effective visibility enhancement method, which contains a coarse-to-fine framework of spatially-smooth illumination estimation. In particular, the refined illumination is effectively generated by optimizing a novel structure-preserving variational model on the coarse version, estimated through the Max-RGB method. The proposed variational model has the capacity of suppressing the textural details while preserving the main structures in the refined illumination map. To further boost imaging performance, the refined illumination is adjusted through the Gamma correction to increase brightness in dark regions. We then estimate the refined reflection map by implementing the joint denoising and detail boosting strategies on the original reflection. In this work, the original reflection is yielded by dividing the input image using the refined illumination. We finally produce the enhanced image by multiplying the adjusted illumination and the refined reflection. Experiments on synthetic and realistic datasets illustrate that our method can achieve comparable results to the state-of-the-art techniques under different imaging conditions.