A Method to Improve the Lining Images Quality in Complex Tunnel Scenes

The lining image collected by the tunnel detection equipment will be degraded by the uneven gray distribution of the collected image due to the restriction of the site environment and hardware resources of the tunnel. In serious cases, the whole image is dim and fuzzy, and the disease feature information cannot be identified from the image background. In order to solve these problems, this paper proposes an image adaptive smoothing and image high frequency edge preserving optimization algorithm for tunnel lining environment. Compared with the traditional image preprocessing and image denoising algorithm, this algorithm improves the problem of the disease gray feature information jumping and information loss in the tunnel lining image due to the imbalance of gray level and the noise interference, and ensures the effectiveness of the original image interested in the disease target area information. Compared with a large number of experimental data, the improved algorithm has a great improvement in convergence speed and image quality.