| 摘 要： |
The numerous photos captured by low-price Internet of Things (IoT) sensors are frequently affected by meteorological factors, especially rainfall. It causes varying sizes of white streaks on the image, destroying the image texture and ruining the performance of the outdoor computer vision system. Existing methods utilise training with pairs of images, which is difficult to cover all scenes and leads to domain gaps. In addition, the network structures adopt deep learning to map rain images to rain-free images, failing to use prior knowledge effectively. To solve these problems, we introduce a single image derain model in edge computing that combines prior knowledge of rain patterns with the learning capability of the neural network. Specifically, the algorithm first uses Residue Channel Prior to filter out the rainfall textural features then it uses the Feature Fusion Module to fuse the original image with the background feature information. This results in a pre-processed image which is fed into Half Instance Net (HINet) to recover a high-quality rain-free image with a clear and accurate structure, and the model does not rely on any rainfall assumptions. Experimental results on synthetic and realworld datasets show that the average peak signal-to-noise ratio of the model decreases by 0.37 dB on the synthetic dataset and increases by 0.43 dB on the real-world dataset, demonstrating that a combined model reduces the gap between synthetic data and natural rain scenes, improves the generalization ability of the derain network, and alleviates the overfitting problem. |
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