Image Enhancement Under Transformer Oil Based on Multi-channel Feature Fusion

Clear images captured under transformer oil are crucial data sources for internal fault diagnosis of oil-immersed transformer. However, images captured under transformer oil suffer from color distortion, low brightness and detail distortion. In order to solve the above problems, a novel multi-channel feature fusion algorithm is proposed in this paper. The first channel image is derived from the gridded gray world white balance algorithm and the adaptive dynamic color channel compensation algorithm proposed in this paper. The second channel image is derived from the standard deviation weighted multi-scale retinex with color restoration (MSRCR) proposed in this paper. The third channel image is derived from the Laplace sharpening based on the second channel image. Then, the saliency weight and brightness weight of the three channel images are calculated respectively, and the weights are normalized. Finally, the three channel images and the corresponding weightes are fused using the multi-scale fusion Gaussian pyramid principle to obtain a clear image under the transformer oil. The experimental results show that the proposed algorithm can better solve the problems of color distortion, detail distortion and low brightness under transformer oil than the comparison methods. The UIQM value of the enhanced images is 115.483% higher than the original images, the UCIQE value is 33.517% higher than the original images, the NIQE value of the enhanced images is 16.420% lower than the original images.