Research on infrared sequence image denoising based on multi-frame averaging and improved bilateral filtering
Huaqiang Wang, Junsheng Shi, Huaping Zhang, Bingyi Xiong, Jing Liang · 2022
Infrared imaging systems have been widely used in military and civil fields. However, the degradation of imaging quality is constantly affected by stripe noises. The traditional mean filtering, median filtering, Gaussian filtering, Wiener filtering and other algorithms have dependence on different noise images, and the image edge is blurred by denoising. The popular bilateral filter takes a lot of calculation due to a two-dimension way and floating point spatial proximity factor. In this paper, an infrared sequence image denoising method based on multi-frame averaging and improved bilateral filtering is introduced. An improved bilateral filter with an integer spatial proximity factor is designed, which is realized by one dimension filtering in horizontal and vertical directions. First, basal stripe in each frame image is removed by two-point correction, and the improved bilateral filter is used to smooth the noise and protect the edge of the image at the same time. Second, random noise is further removed by averaging multi-frame with a set of 25 images. The experimental results show that the proposed denoising method can effectively remove the noise and better maintain the edge structural information of the image.