Global and Local Contrast Adaptive Enhancement Methods for Low-Quality Substation Equipment Infrared Thermal Images

Noncontact infrared detection is an effective way to diagnose substation equipment defects, but inclement weather conditions like haze and rainstorms produce low-quality infrared images, making it challenging to recognize equipment. To overcome this problem, a global and local contrast adaptive enhancement method is suggested. The global approach consists of the gray-level stretching Retinex (GLS Retinex) and the weighted truncated Gaussian histogram equalization (WTGHE). The GLS Retinex comprises gray-level stretching, fast-guide filtering, and parameter self-adjustment Retinex, which can be used to brighten dark images. The WTGHE contains Gaussian histogram calculation, truncated histogram equalization (HE), and weighted RGB fusion, aiming to handle blurred images. To significantly improve the quality of images in some detailed regions that cannot be handled by global enhancement algorithms, a local scheme called regional growth reenhancement is proposed, which includes multiseed region automatic selection and adaptive threshold growth criterion. The performance of the proposed technique is evaluated using over 1000 infrared thermal photographs of several substations under extremely difficult circumstances. The experimental results show that the novel strategy outperformed existing enhancement methods. The deep-learning detector also demonstrated the effectiveness of our proposed enhancement method for the improvement of detection precision.