High Dynamic Range Imaging by Perceptual Logarithmic Exposure Merging

TL;DR

This paper introduces a novel HDR imaging method based on the LTIP model, which aligns with the human visual system, enabling effective exposure merging that yields high-quality images.

Contribution

It establishes a unifying framework linking LTIP and HVS models, demonstrating that HDR merging under LTIP is equivalent to irradiance map fusion, with improved image quality.

Findings

HDR algorithm achieves high subjective quality

Objective evaluations confirm the method's effectiveness

Unifying framework simplifies HDR image processing

Abstract

In this paper we emphasize a similarity between the Logarithmic-Type Image Processing (LTIP) model and the Naka-Rushton model of the Human Visual System (HVS). LTIP is a derivation of the Logarithmic Image Processing (LIP), which further replaces the logarithmic function with a ratio of polynomial functions. Based on this similarity, we show that it is possible to present an unifying framework for the High Dynamic Range (HDR) imaging problem, namely that performing exposure merging under the LTIP model is equivalent to standard irradiance map fusion. The resulting HDR algorithm is shown to provide high quality in both subjective and objective evaluations.