Abstract: The embodiments are directed towards providing personalized federated learning (PFL) models via sharable federated basis models. A model architecture and learning algorithm for PFL models is disclosed. The embodiments learn a set of basis models, which can be combined layer by layer to form a personalized model for each client using specifically learned combination coefficients. The set of basis models are shared with each client of a set of the clients. Thus, the set of basis models is common to each client of the set of clients. However, each client may generate a unique PFL based on their specifically learned combination coefficients. The unique combination of coefficients for each client may be encoded in a separate personalized vector for each of the clients.

Abstract: An image enhancement method for improving color perception of colorblind viewers has an image input step, an image difference area analyzing step, an image color distribution adjusting step, and an image output step. In such method, a normal image and a colorblindness-simulative image are input and calculated to produce a colorblindness-optimized image. Color vision obtained by a colorblind person from the colorblindness-optimized image is substantially identical to that obtained by a person with normal color perception from the normal image.

Abstract: An image enhancement method for improving color perception of colorblind viewers has an image input step, an image difference area analyzing step, an image color distribution adjusting step, and an image output step. In such method, a normal image and a colorblindness-simulative image are input and calculated to produce a colorblindness-optimized image. Color vision obtained by a colorblind person from the colorblindness-optimized image is substantially identical to that obtained by a person with normal color perception from the normal image.