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The inherent impairments of visible light communication (VLC) in terms of nonlinearity of light-emitting diode (LED) and the optical multipath restrict bit error rate (BER) performance. In this paper, a model-driven deep learning (DL) equalization scheme is proposed to deal with the severe channel impairments. By imitating the block-by-block signal processing block in orthogonal frequency division multiplexing (OFDM) communication, the proposed scheme employs two subnets to replace the signal demodulation module in traditional system for learning the channel nonlinearity and the symbol de-mapping relationship from the training data. In addition, the conventional solution and algorithm are also incorporated into the system architecture to accelerate the convergence speed. After an efficient training, the distorted symbols can be implicitly equalized into the binary bits directly. The results demonstrate that the proposed scheme can address the overall channel impairments efficiently and can recover the original symbols with better BER performance. Moreover, it can still work robustly when the system is complicated by serious distortions and interference, which demonstrates the superiority and validity of the proposed scheme in channel equalization.

For the characteristics of synthetic aperture radar (SAR) images, such as the dense arrangement of ship targets on shore, which are easily affected by land, the sparse distribution of small ships in the deep sea, which are easily missing detect, and also the existence of a lot of negative sample background areas. We propose a new method based on improved YOLOX as Anchor Free target detection method, which greatly improves the training efficiency compared with the preset anchor box. In view of the problem that densely packed ship targets are easy to miss, and the ship target in the deep sea is weak and the distribution is sparse. We propose improved corner efficient intersection over union (ICEIOU) to further comprehensively consider the regression parameters, and the loss function is optimized for the network training process. We use Adaptive-NMS to adaptively adjusts the non-maximum suppression (NMS) threshold value for the dense arrangement and sparse distribution of ships, and atrous convolution is used to improve the problem of some detailed information loss. And combined with coordinate attention mechanism, high-speed and high-precision ship target detection is realized. The experimental results show that compared with the original YOLOX method, the detection precision of the method in this paper on HRSID dataset is improved from the original 88.81% to 91.48%, and the mAP index is improved from the original 87.72% to 91.76%, which is obviously better than the comparison method.