红外图像具有动态范围窄、对比度低、易受噪声污染等缺点，传统红外图像去噪算法在去除噪声的同时也滤掉了图像细节。提出了一种基于稀疏表示的红外图像去噪新方法。该方法首先将原始红外图像进行聚类分析，再将每一聚类子图像分解成字典，由稀疏系数矩阵重构去噪后的红外图像。实验结果表明，该方法相比于传统红外图像去噪算法，能更好地保留图像的细节信息，视觉效果比较理想。

Infrared images have the disadvantages of narrow dynamic range, low contrast and being easy to be polluted by noise. However, traditional infrared image denoising algorithms may filter out image details while removing noise. A new infrared image denoising method based on sparse representation is proposed. The method firstly clusters an original infrared image; secondly decomposes each cluster sub-image into a dictionary; and then the denoised infrared image is reconstructed from the sparse coefficient matrix. The experimental results show that this method can retain image details better than the traditional infrared image denoising algorithm and the visual effect is ideal.