Camera Exposure Control for Robust Robot Vision with Noise-Aware Image Quality Assessment
This work addresses exposure control for robot vision systems, offering an incremental improvement in image quality for robotics applications.
The authors tackled the problem of robust robot vision by proposing a noise-aware exposure control algorithm that captures well-exposed images to enhance computer vision and robotics tasks, achieving higher qualitative and quantitative performance compared to conventional methods.
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.