  In this paper, we propose a noise-aware exposure control algorithm for robust
robot vision. Our method aims to capture the best-exposed image which can boost
the performance of various computer vision and robotics tasks. For this
purpose, we carefully design an image quality metric which captures
complementary quality attributes and ensures light-weight computation.
Specifically, our metric consists of a combination of image gradient, entropy,
and noise metrics. The synergy of these measures allows preserving sharp edge
and rich texture in the image while maintaining a low noise level. Using this
novel metric, we propose a real-time and fully automatic exposure and gain
control technique based on the Nelder-Mead method. To illustrate the
effectiveness of our technique, a large set of experimental results
demonstrates higher qualitative and quantitative performances when compared
with conventional approaches.
