A high fidelity contrast improving model based on human vision mechanisms

We propose a computational model of adjusting color contrast and luminance contrast nonlinearly, dynamically and adaptively based on the image content. It uses lateral inhibition mechanisms and adaptation (light/dark, color) mechanisms. Our model can improve the appearance of an image naturally, both locally and globally at the same time. Our transformation shows a reverse-S-shape response, with the curvature being determined based on the content characteristics of each individual image. We demonstrate the performance by image enhancement experiments. It shows good performance in not only luminance, but also chromaticity. Our model and transformation can be applied to many fields, especially, storing or repairing images of art or cultural heritage, because it can improve images preserving original subjective impression