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

TL;DR

This paper introduces a noise-aware, real-time exposure control algorithm for robot vision that optimizes image quality by balancing sharpness, texture, and noise, thereby enhancing computer vision tasks.

Contribution

It presents a novel, lightweight image quality metric combining gradient, entropy, and noise, and a real-time exposure control method based on Nelder-Mead optimization, improving robustness in robot vision.

Findings

Higher qualitative image quality compared to conventional methods

Quantitative improvements in image sharpness and noise reduction

Effective real-time automatic exposure adjustment

Abstract

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.