A Deep Regression Model for Tongue Image Color Correction Based on CNN

Different viewing or shooting situations can affect color authenticity and generally lead to visual inconsistencies for the same images. At present, deep learning has gained popularity and opened up new avenues for image processing and optimization. In this paper, we propose a novel regression model named TococoNet (Tongue Color Correction Network) that extends from CNN (convolutional neural network) to eliminate the color bias in tongue images. The TococoNet model consists of symmetric encoder--decoder U-Blocks which are connected by M-Block through concatenation layers for feature fusion at different levels. Initially, we train our model by simulatively introducing five common biased colors. The various image quality indicators holistically demonstrate that our model achieves accurate color correction for tongue images, and simultaneously surpasses conventional algorithms and shallow networks. Furthermore, we conduct correction experiments by introducing random degrees of color bias, and the model continues to perform well for achieving excellent correction effects. The model achieves up to 84% correction effectiveness in terms of color distance ΔE for tongue images with varying degrees of random color cast. Finally, we obtain excellent color correction for actual captured images for tongue diagnosis application. Among these, the maximum ΔE can be reduced from 30.38 to 6.05. Overall, the TococoNet model possesses excellent color correction capabilities, which opens promising opportunities for clinical assistance and automatic diagnosis.