Visual impairment is one of the most serious social and public health problems in the world, therefore, it is of great theoretical and practical significance to study the image enhancement algorithms for the visually impaired, which is the basis for the development of assistive devices. In this paper, a general deep learning based image enhancement framework for the visually impaired is proposed, which can be used to enhance images to compensate for any visually impaired symptom that can be modeled. Take central vision loss as an example, we first model the central vision loss based on the contrast sensitivity function (CSF) specified by clinical indicator Pelli-Robson score and logMAR visual acuity, and then use the proposed framework to generate an image enhancement
method aiming at compensating for the central vision loss. Both the simulation experiment and the patient experiment show the superiority of the proposed image enhancement method designed for the central vision loss, which also validates the effectiveness of the proposed framework.