Deriving filter parameters using dual-images for image de-noising
Lingyu Wang, Graham Leedham, Siu‐Yeung Cho · 2007
This paper presents a novel technique to derive the filter parameters for removing signal dependent noise (SDN) in the image. In order to remove SDN, many de-noising algorithms rely on aprioriknowledge of noise parameters, especially the variance sigman2, and the gamma value gamma of the specific imaging technique. This paper proposes a technique to automatically derive the signal variance sigmaf2and use this parameter to construct the Local.LinearMinimumMeanSquareError(LLMMSE) filter without the need to know the values of sigman2and gamma. Two image instances of the same noisy scene are used to calculate the signal variance which is then used to construct the LLMMSE filter. Experiments with both the "Lena" image and real-life far-infrared (FIR) vein pattern images showed that the proposed technique can predict the signal variance consistently, and the constructed LLMMSE filter performs well in removing the signal dependent noise.