变压器油下图像增强算法

为解决变压器检测机器人在变质、变色的变压器油内部采集的图像存在色彩失真、对比度低等问题,提出一种变压器油下图像融合增强算法.首先,利用完美反射算法对图像进行白平衡处理,以消除油下光照强度不均匀对图像颜色的影响,使得色彩更加均衡;然后,对色彩校正的图像进行自适应伽马校正,以提高图像的对比度;最后,采用多尺度融合策略将色彩校正后的图像与自适应伽马校正处理后的图像进行融合,得到变压器油下清晰的图像.实验结果表明,经所提出算法处理后的变压器油下图像色彩鲜明、细节丰富,与原始图像相比,图像质量评价指标(UCIQE)、特征点匹配个数以及信息熵均有显著提高,能够为变压器内部故障检测提供清晰的数据.

Image enhancement algorithm under transformer oil

In order to solve the problems of color distortion and low contrast in the images collected by the transformer detection robot in the deteriorated and discolored transformer oil, an image fusion enhancement algorithm for transformer oil is proposed. Firstly, the image is white-balanced by the perfect reflection algorithm to eliminate the influence of uneven illumination intensity under oil on the image color and make the color more balanced. Then, the color-corrected image is subjected to adaptive gamma correction to improve the contrast of the image. Finally, the multi-scale fusion strategy is adopted to fuse the image after color correction and the image after adaptive gamma correction, and a clear image under transformer oil is obtained. The experimental results show that the images processed by this algorithm are bright in color and rich in details. Compared with the original images, the image quality evaluation index (UCIQE), feature point matching and information entropy are significantly improved, which can provide clear data for transformer internal fault detection.