Infrared image denoising based on the variance-stabilizing transform and the dual-domain filter

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. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a denoising method for infrared images based on VST and DDF, which effectively suppresses mixed Poisson-Gaussian noise and retains abundant details. Quality and quantity comparisons show that the proposed method outperforms other existing methods.