Ukcheol Shin (KAIST), Jinsun Park (KAIST), Gyumin Shim (KAIST), Francois Rameau (KAIST), In So Kweon (KAIST)

Abstract

In this paper, we propose a noise-aware exposure control algorithm for robust robot vision. Our method aims to capture best-exposed images, which can boost the performance of various computer vision and robotics tasks. For this purpose, we carefully design an image quality metric that 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 the preservation of sharp edges 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 the higher qualitative and quantitative performance compared with conventional approaches.

Publication

"Camera Exposure Control for Robust Robot Vision with Noise-Aware Image Quality Assessment" [PDF]

Ukcheol Shin, Jinsun Park, Gyumin Shim, Francois Rameau, and In So Kweon.

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019

Acknowledgments

This work was supported in part by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT).