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In this paper, we propose a noise-aware exposure control algorithm for robustrobot vision. Our method aims to capture the best-exposed image which can boostthe performance of various computer vision and robotics tasks. For thispurpose, we carefully design an image quality metric which capturescomplementary 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 edgeand rich texture in the image while maintaining a low noise level. Using thisnovel metric, we propose a real-time and fully automatic exposure and gaincontrol technique based on the Nelder-Mead method. To illustrate theeffectiveness of our technique, a large set of experimental resultsdemonstrates higher qualitative and quantitative performances when comparedwith conventional approaches.

Camera Exposure Control for Robust Robot Vision with Noise-Aware Image Quality Assessment