Thanks for your response Ayush, but I would like to disagree to your opinion. It might be true that SMOTE may have computational challenges dealing with image dataset, especially when it is large in dimensions, but there has been many examples, not just mine, that SMOTE has proved to solve the majority class bias problem for imbalanced image dataset. The results are remarkable so far. And much better than the other techniques mentioned in this article.

My recommendation to you will be try out SMOTE yourself for a practical problem of yours with a highly imbalanced image dataset and then let me know if you see any improvement in results. And FYI I have tried this in industrial problem scenarios as well, and SMOTE has not disappointed me so far :)
The computational drawback of SMOTE can be dealt by resizing your image data to a downsampled version, provided important information is not being lost.

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