Two-stage convolutional neural network for joint removal of sensor noise and background interference in lock-in thermography

Lock-in thermography provides a fast, low-cost, and accurate non-destructive testing for subsurface defect detection in materials. However, there is serious sensor noise and background interference presented in the infrared images captured by lock-in thermography. In this paper, we set up lock-in thermography experiments to analyze the characteristics of sensor noise and background interference. Based on their different characteristics, we design a two-stage CNN model for the joint removal of sensor noise and background interference. This model consists of a number of multi-direction feature extraction blocks (MDFEBs) to extract local features in different directions for pixel-level sensor noise removal, and an encoder–decoder architecture with large receptive fields to extract global features for background interference correction. Experimental results demonstrate that our proposed method outperforms the well-established infrared image denoising and non-uniform background correction techniques on specimens made of different materials and at various excitation frequencies. The proposed two-stage CNN model provides high-quality input of infrared images for the following subsurface defect detection task in lock-in thermography.