Symptomatic vitreous opacity image enhancement using linear and nonlinear transformations

Abstract

The rapid development of digital ophthalmology has put forward new demands for symptomatic vitreous opacity image processing, and symptomatic vitreous opacity image enhancement has become a key issue in the research of digital ophthalmology development. Focusing on this key issue, this paper proposes a linear and nonlinear transform-based image enhancement for symptomatic vitreous opacity. Specifically, firstly, a sharpening and white balance correction module is proposed, and the symptomatic vitreous opacity image is sharpened to compensate for the loss of details in the exposed area. Secondly, a visibility restoration module based on type II fuzzy sets is designed. We propose a symptomatic vitreous opacity image enhancement method with interpretability. As a result, the proposed method can solve the exposure and low visibility problems occurring in the symptomatic vitreous opacity image, and produce enhancement results that conform to human visual characteristics and have interpretability. Through comparative experimental analysis, the proposed method achieves superior results compared to advanced image enhancement methods.