Infrared Image Enhancement Method Based on Multiscale Weighted Guided Filtering

Existing infrared images have problems on fuzzy details, unclear edges, and texture. This paper proposed an infrared image enhancement method based on weighted guided filtering to solve these problems. First, multiscale weighted guided filtering with steering kernel layered the input images. It obtained detailed layer images and a base layer image. Subsequently, maximum posterior probability algorithm based on Markov-Poisson and Gamma correction algorithms enhanced the detailed layer images. Adaptive histogram equalization algorithm with limited contrast stretched the contrast of the base layer image. Finally, enhanced images were obtained through linear fusion. The subjective and objective experimental results show that the proposed method had good detail enhancement effects, and the edges and texture information of the processed images were relatively prominent. The proposed method had better calculation results for information entropy(IE), entropy enhancement(EME), and mean gradient (AG). It satisfies the requirements for enhanced infrared images and clear edge textures.