In infrared image processing, owing to technical issues with the infrared detector, the original infrared image includes a variety of noise, especially salt and pepper noise, fixed noise, or random stripe noise. Currently, there are many filtering algorithms for infrared image denoising, but they emphasize time, space, denoising effect, maintaining detail, and so on differently; therefore, it is difficult to achieve a perfect combination. Identifying methods to filter noise information more quickly, efficiently, and accurately and retain more details is an important future research direction for noise reduction in infrared image processing. This study investigated and compared the current mainstream infrared image denoising algorithms from three categories: traditional filter denoising, transform domain filter denoising, and image layered processing filter denoising, and a combination of a traditional algorithm and image layered adaptive denoising algorithm is proposed to provide a reference for future studies in related fields.