Principal component fusion based unexposed biological feature enhancement of fundus images

In the field of ophthalmology, digital images play an important role for automatic detection of various kind of eye diseases. Digital images in the field image enhancement are the first stage to assisting ophthalmologist for diagnosis. As a result, various algorithms, and methods for the enhancement of retinal images have been developed, which may face obstacles that are common in augmentation processes, such as false edges and weak illuminated that obscure image particulars. To eliminate such issues, this paper projected a novel framework for unexposed retinal image. The proposed paper uses multiscale Gaussian function for estimation of illumination layer from unexposed color retinal image and then it is corrected by gamma method. Further to this, the principal component analysis (PCA) is utilized here to generate fused enhance result for unexposed retinal images. Then, contrast limited technique is employed here for further edge and contextual details improvement. When compared to several enhancement-based state-of-the-art procedures, experimental results show that the suggested method produces results with good contrast and brightness. The significance of the proposed method that this method may help ophthalmologists screen for unexposed retinal illnesses more efficiently and build better automated image analysis for healthcare diagnosis.