Chest X-Ray image denoising using Nesterov optimization method with total variation regularization

Abstract

We propose a chest X-Ray image denoising method based on Total variation regularization with implementation on the Nesterov optimization method. The denoising problem is formulated in the form of the second-order cone programming problem and then it is transformed to a saddle point problem under the min-max form. The chest X-Ray images are also processed by the Anscombe transform to be appropriate for the formulated denoising problem. In the experiments, we test on chest X-Ray images of the Radiopaedia dataset. Denoising results are evaluated by using the Peak signal-to-noise ratio and the Structure similarity metrics. Based on the image quality assessment metrics, we compared images quality after denoising of the proposed method with ones of other similar denoising methods. The results confirmed that the proposed method outperforms other compared denoising methods.