### Infrared image enhancement algorithm based on adaptive weighted guided filter

• Received:2021-01-28 Revised:2021-07-09 Online:2022-04-26 Published:2022-04-26
• Supported by:
This work was supported by the National Natural Science Foundation of China (61673017, 61905285), and the Shaanxi
Provincial Department of Science and Technology Key Project in the Field of Industry (2018ZDXM-GY-039).

Abstract:

The physical principle of infrared imaging leads to the low contrast of the whole image, the blurring of contour and edge details, and it is also sensitive to noise. To improve the quality of infrared image and visual effect, an adaptive weighted guided filter (AWGF) for infrared image enhancement algorithm was proposed. The core idea of AWGF algorithm is to propose an adaptive strategy to update the weights of guided filter (GF) parameters, which not only improves the accuracy of regularization parameter estimation in GF theory, but also achieves the purpose of removing infrared image noise and improving its detail contrast. A large number of real infrared images were used to verify AWGF algorithm, and good experimental results were obtained. Compared with other guided filtering algorithms, the halo phenomenon at the edge of infrared images processed by the AWGF algorithm is significantly avoided, and the evaluation parameter values of information entropy (IE), average gradient (AG), and moment of inertia (MI)are relatively high. This shows that the quality of infrared image processed by the AWGF algorithm is better.

Key words: infrared image, guided filter (GF), adaptive weight, image enhancement, regularization parameter