Learning-based tone mapping operator for image matching
In this paper, we propose a new framework to optimally tone-map a high dynamic range (HDR) content for image matching under drastic illumination variations. This task is of fundamental importance for many computer vision applications. To design such a framework, we build a luminance invariant guidance model using a Support Vector Regressor (SVR) and learn it to facilitate the extraction of invariant descriptors from scenes subject to wide variety of appearance changes such as day/night transition. To this end, we initially generate appropriate training samples using a simple similarity-maximization mechanism. We then employ the learned model to predict optimal modulation maps that help to locally alter the intrinsic characteristics (such as shape, size) of the tone mapping function. We evaluate the proposed model performance in terms of matching score and mean average precision rate using state-of-the-art descriptor extraction schemes. We demonstrate that our tone mapping framework significantly outperforms the existing perceptually-driven state-of-the-art TMOs on the benchmark datasets.