Lightweight image denoising method for feature-based visual SLAM in γ radiation environments

Abstract

In the γ radiation environments, high-energy photons impact image sensor pixels and induce speckle noise in the scene image, which influences the accuracy of feature detection and matching in the visual Simultaneous Localization and Mapping (SLAM) algorithms. To mitigate these effects, this paper presents a lightweight image denoising method based on super-neighborhood down-sampling. Firstly, the super-neighborhood down-sampling strategy is used to split the speckle noise in the camera images into isolated point noises in the sub-image sets. Secondly, these isolated flickering point noises are removed by the detection-based median filter. Finally, the denoised scene image is reconstructed by the super-neighborhood up-sampling. Extensive experiments were performed on the radiation environment datasets that were captured by the camera inside the Cobalt-60 hot cell. The results show that the proposed method can effectively filter out the speckle radiation noise induced by γ rays, significantly improve the quality of features and feature matching rate, and improve the performance of ORB-SLAM3, the state-of-the-art feature-based SLAM algorithm, in radiation environments.