Noise Estimation Technique for Multiple Copies Image Denoising
Somkait Udomhunsakul, Napa Sae-Bae · 2016
This paper proposed a noise estimation technique when multiple noisy image copies are available. In particular, the proposed technique estimated noise variance of one noisy image using the information of an original image from another copy of noisy images. Consequently, this proposed noise estimation could be used in conjunction with the state-of-the-art denoising algorithms to improve the quality of recovered images in terms of PSNR. Experiments are performed on two widely-used image datasets in wide range of noise variances to confirm the accuracy of the proposed estimation technique. The result also reveals that, when the noise variance is small, the estimation derived from the traditional method is of high variation, depending on the image content. In this case, the proposed method achieves much better accuracy whereby resulting in a large improvement of recovered image quality. In addition, the results confirm that when they are two noisy image copies, thresholding before fusing the images leads to the recovered image with better quality.