关键词: 图像处理, 红外图像去噪, 深度迁移学习, 卷积神经网络, 气体泄漏检测

Abstract: Uncooled infrared cameras are widely used in the field of gas leak detection due to the advantages of low cost, long life and stable performance. An excellent image denoising algorithm can effectively improve the sensitivity and accuracy of detection. Combining deep learning and transfer learning techniques, an infrared image denoising method for gas leakage based on deep transfer learning is proposed in this work. Firstly, the convolutional neural network model is trained using a static scene dataset. Then some model parameters are fixed, and the model is retrained through simulating the gas dataset. Finally, a model suitable for denoising infrared images of gas leakage is obtained. The experimental results show that the method can denoise gas infrared images captured by uncooled infrared camera. The denoised images have obvious gas profile information, and the location of the leak source can be distinguished at the same time. Therefore, it is believed that the proposed infrared image denoising method can benefit uncooled infrared cameras better accomplish the task of gas leak detection.

Key words: image processing, infrared image denoising, deep transfer learning, convolutional neural networks, gas leak detection