Identification and Removal of Light Pollution in Maritime Night-Time Images Based on Spatial Frequency Blocks - Hui Sun; Jingyang Wang; Mingyang Pan; Zongying Liu; Shaoxi Li; Ruolan Zhang; Yang Wei (Journal of Marine Science and Engineering)
The safety of ships during nighttime navigation has always been a major concern. With the widespread application of technologies such as intelligent recognition, intelligent detection, and unmanned ship navigation at night, nighttime maritime light pollution has significantly affected the effectiveness of these intelligent technologies and navigation safety. Therefore, effectively eliminating nighttime maritime light pollution has become an urgent challenge that needs to be addressed. This paper presents a model based on spatial frequency blocks (SFBs) to solve the problem of light pollution in nighttime sea images. The model includes ResNet-50, an encoder, a decoder, and a discriminator. To enable the model to better remove the influence of light pollution, this study designs a method of first detecting the light pollution area and then removing it. It extracts image information from the space–frequency domain to help eliminate light pollution and retain more image information. The experimental results show that on the nighttime light pollution dataset, the Peak Signal-to-Noise Ratio (PSNR) of the model is improved to 24.91 compared to the current state-of-the-art image restoration model, while the Frechet inception distance (FID) is reduced to 64.85. At the same time, in the real night environment, the model can better remove light pollution to recover the original nighttime information. It has excellent performance and provides a certain reference value for advancing the safety of nighttime maritime navigation.