Learning-based adaptive tone mapping for keypoint detection
The goal of tone mapping operators (TMOs) has traditionally been to display high dynamic range (HDR) pictures in a perceptually favorable way. However, when tone-mapped images are to be used for computer vision tasks such as keypoint detection, these design approaches are suboptimal. In this paper, we propose a new learning-based adaptive tone mapping framework which aims at enhancing keypoint stability under drastic illumination variations. To this end, we design a pixel-wise adaptive TMO which is modulated based on a model derived by Support Vector Regression (SVR) using local higher order characteristics. To circumvent the difficulty to train SVR in this context, we further propose a simple detection-similarity-maximization model to generate appropriate training samples using multiple images undergoing illumination transformations. We evaluate the performance of our proposed framework in terms of keypoint repeatability for state-of-the-art keypoint detectors. Experimental results show that our proposed learning-based adaptive TMO yields higher keypoint stability when compared to existing perceptually-driven state-of-the-art TMOs.