In the past decades, image denoising has been widely studied as a basic technology for image processing. However, most denoising methods are designed for Gaussian noise, while few researches focus on the suppression of mixed Poisson-Gaussian noise, which usually found in infrared images. In order to remove the mixed noise in infrared images, this paper proposes a denoising method based on the variance-stabilizing transform (VST) and the dual-domain filter (DDF). We transform the mixed noise data into an approximate Gaussian distribution with uniform variance through the VST and then adopt the improved DDF to denoise the transformed data. Finally, we apply the closed approximation inverse of the VST to the denoised data for the final denoising estimate. The denoising results of infrared images with different intensity all effectively suppress the mixed noise and retain abundant details. The quality and quantity comparisons with nine other methods reveal that our method can achieve a superior performance.

在过去的几十年中，图像去噪作为图像处理的基本技术已被广泛研究。但是，大多数降噪方法都是针对高斯噪声而设计的，而很少有研究专注于抑制通常在红外图像中发现的混合泊松-高斯噪声。为了消除红外图像中的混合噪声，本文提出了一种基于方差稳定变换（VST）和双域滤波器（DDF）的去噪方法。我们通过VST将混合噪声数据转换为具有均匀方差的近似高斯分布，然后采用改进的DDF对转换后的数据进行降噪。最后，我们将VST的闭合近似逆应用于去噪后的数据，以得到最终的去噪估计。不同强度的红外图像的去噪效果均能有效抑制混合噪声并保留大量细节。与其他九种方法的质量和数量比较表明，我们的方法可以实现出色的性能。