A Retina Inspired Model for High Dynamic Range Image Rendering

Abstract

We propose a new tone mapping model to render high dynamic range (HDR) images in limited dynamic range devices in this paper. This neural network model is inspired by the retinal information processing mechanisms of the biological visual system, including the adaptive gap junction between horizontal cells (HCs), the negative HC-cone feedback pathway, and the center-surround antagonistic receptive fields of bipolar cells (BCs). The key novelty of the proposed model lies in the adaptive adjustment of the receptive field size of HCs based on the local brightness, which simulates the dynamic gap junction between HCs. This enables the brightness of distinct regions to be recovered into clearly visible ranges while reducing halo artifacts common to other methods. The BCs serve to enhance the local contrast with their center-surround RF structure. By comparing with the state-of-the-art tone mapping methods qualitatively and quantitatively, our method shows competitive performance in term of improving details in both dark and bright areas.