Real-time Image Enhancement for Emergency Rescue Scenarios in Smart Grids

Image enhancement technology plays an important role in the practical application of detecting underwater tunnels. By improving image quality, it enhances the accuracy and reliability of detection results. However, in deep-sea scenes, the problem of image color distortion and detail loss is common. To address this issue, a real-time underwater image enhancement algorithm based on weighted multi-branch correction has been proposed in this paper. The algorithm uses a combination of multi-scale and multi-branch techniques to process the image. The multi-branch correction module has been improved by increasing the weighting and downsampling strategy, which not only improves the processing speed but also ensures the processing effect. In the air-frequency domain interactive processing module, zero padding and window functions have been introduced to reduce the ringing effect and block effect. In addition, the smart grid system provides stable power support for underwater tunnel search and rescue, which ensures the safety of the search and rescue environment and also provides a good experimental lighting environment for the image enhancement algorithm. At the same time, the model has been trained and validated using self-constructed real deep-sea datasets. The existing algorithms and the algorithm proposed in this paper have been compared and analyzed comprehensively. The experimental results show that the algorithm proposed in this paper is better than the existing algorithms in terms of processing effect, and the processing speed is improved by about 12.7%.