Subjective Quality Assessment of Enhanced Retinal Images

Many retinal images sometimes suffer from uneven illumination, which influences the analysis and diagnosis of retinal diseases. To improve the image quality of those retinal images, one feasible solution is to utilize low-light image enhancement (LIE) algorithms. However, how to evaluate the perceptual quality of enhanced retinal images (ERIs) generated by different LIE algorithms remains a challenging problem. In this paper, we conduct subjective experiments to investigate the quality assessment of ERIs. First, we collect 250 retinal images with the authentic low-light distortion, and then adopt eight LIE algorithms to produce 2000 ERIs. Second, a subjective experiment is conducted, resulting in the proposed Enhanced Retinal Image Quality Assessment Database (ERIQAD). Finally, we test some well-known no reference image quality assessment (NR IQA) methods on our proposed ERIQAD. Experimental results demonstrate that existing mainstream NR IQA methods merely achieve ordinary performance to predict the perceptual quality of ERIs.