Dental image enhancement network may facilitate early diagnosis of oral and dental diseases

Hence researchers proposed a novel and adaptive dental image enhancement strategy based on a small dataset and a paired branch Denticle-Edification network (Ded-Net). The input dental images are reflected and illuminated using a multilayer Denticle network called the De-Net. Any unseen degradation of reflection or illumination is removed by subsequent enhancement operations. An Edification network called the Ed-net is used to maintain adaptive illumination consistency.

The network is regularized after determining that the input data's decomposition is congruent, and it offers the user-specific adaptation flexibility they seek for desired contrast levels. Luminance in the final image is balanced by the practical trade-off. The advantage of this proposed method is that it predicts dynamically in resilient conditions without any requirement for large-scale / paired or carefully selected datasets.

Thus, the experimental results of this proposed study improved visibility and contrast and preserved the edges and boundaries of the low-contrast input images. They also suggested that future dental imaging can be modified by these intelligent and expert system applications.

Further reading: Khan, R., Akbar, S., Khan, A. et al. Dental image enhancement network for early diagnosis of oral dental disease. Sci Rep 13, 5312 (2023).https://doi.org/10.1038/s41598-023-30548-5