非制冷红外图像降噪算法综述

红外图像处理中，由于非制冷红外探测器工艺技术上的原因，原始的红外图像包含多种噪声，尤其是椒盐噪声、固定或随机条纹噪声。当前有许多红外图像降噪的滤波算法，但在时间、空间、降噪效果、细节保持等方面各有侧重，难以实现完美结合。如何更快速、更高效、更准确地滤除噪声信息，保留更多的细节信息，是今后红外图像处理降噪研究的关键方向。本文调研了目前主流的红外图像降噪算法，并从传统滤波降噪、变换域滤波降噪、基于图像分层处理滤波降噪三大类别进行了分析比较，并且提出了一种结合传统算法和基于图像分层的自适应降噪算法，为今后的相关领域研究人员提供参考。

A Survey of Uncooled Infrared Image Denoising Algorithms

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.