  Depth estimation plays a important role in SLAM, odometry, and autonomous
driving. Especially, monocular depth estimation is profitable technology
because of its low cost, memory, and computation. However, it is not a
sufficiently predicting depth map due to a camera often failing to get a clean
image because of light conditions. To solve this problem, various sensor fusion
method has been proposed. Even though it is a powerful method, sensor fusion
requires expensive sensors, additional memory, and high computational
performance.
  In this paper, we present color image and monochrome image pixel-level fusion
and stereo matching with partially enhanced correlation coefficient
maximization. Our methods not only outperform the state-of-the-art works across
all metrics but also efficient in terms of cost, memory, and computation. We
also validate the effectiveness of our design with an ablation study.
