Improving BM3D on non-stationary Gaussian models for real image noise

Most of the work related to image denoising is based on artificial noise of stationary Gaussian distribution (synthetically added to the image in arrears). However, this choice is only a rough approximation of the real noise distribution. That is why, various research works were recently focusing on how to perfectly model the real noise inherent in captured images. In this paper, we model this distribution as non-stationary conditional Gaussian, where the standard deviation is depending on the pixel intensity. Experimental results show that this assumption models the real image noise more accurately (specifically when it comes to its adaptive removal). For that, we developed an extended version of BM3D called NBM3D. It is suitable for adaptive non-stationary noise removal in natural captured images where the original BM3D performance is relatively limited. To accurately verify the denoising performance under real case scenarios, we compute a "noise-free" ground truth image as the average of a sequence of images captured on a static scene by a non-moving monocular camera. Then, we estimate the conditional Gaussian distribution. The resulting model is used for the execution of NBM3D. The comparison between our method and the state-of-the-art BM3D is done by comparing the denoised images using both approaches against the ground truths. Results on three different cameras and 15 sequences with varying lighting conditions show that the proposed NBM3D provides consistent denoising improvements compared to state-of-the-art in terms of real noise removal.