P-CVD-SWIN: a parameterized neural network for image daltonization

Nowadays, about 8 % of men and 0.5 % of women worldwide suffer from color vision deficiency. People with color vision deficiency are mostly dichromats and closely related anomalous trichromats, and are subdivided into three types: protans, deutans, and tritans. Special image preprocessing methods referred to as daltonization techniques allow increasing the distinguishability of chromatic contrasts for people with dichromacy. State-of-the-art neural network architectures involve training separate models for each type of dichromacy, which makes such models cumbersome and inconvenient. In this paper, we propose for the first time a parameterized neural network architecture, which allows training the same neural network model for any type of dichromacy, being specified as a parameter. We named this model P-CVD-SWIN, supposing it a parametrized development of the recently suggested CVD-SWIN model. A generalization of the Vienot dichromacy simulation method was proposed for model training. Experiments have shown that the P-CVD-SWIN neural network parameterized by the type of dichromacy provides better preservation of chromatic naturalness during daltonization, compared to a combination of several CVD-SWIN models, each trained for its own type of dichromacy.