Image Enhancement Under Transformer Oil Based on Multichannel Feature Fusion

Clear images captured under transformer oil are crucial data sources for internal fault diagnosis of oil-immersed transformers. However, images captured under transformer oil suffer from color distortion, low brightness, and detail distortion. In order to solve the above problems, a novel multichannel feature fusion algorithm is proposed in this article. The first channel image is derived from the gridded gray world (GGW) white balance algorithm and the adaptive dynamic color channel compensation algorithm proposed in this article. The second channel image is derived from the standard deviation weighted multiscale retinex with color restoration (SDMSRCR) proposed in this article. 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 weights are fused using the multiscale 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 underwater image quality measure (UIQM) value of the enhanced images is 115.483% higher than the original images, the underwater color image quality evaluation (UCIQE) value is 33.517% higher than the original images, the naturalness image quality evaluator (NIQE) value of the enhanced images is 16.420% lower than the original images.