An Improved Image Denoising Model by Incrementally Learning Gaussian Mixtures Parameters

Patch-based prior learning algorithm is capable of delivering state-of-the-art performance in image denoising. The major concern that affects the patch-based restoration algorithms is the accuracy of the patch priors. Our work is based on the extension of the Gaussian Mixture Model (GMM) estimation which is developed to iteratively capture and process data incrementally. In this paper, we introduce an image denoising framework that uses patch prior learned online refines Gaussian mixture model incrementally. This model decides if we can merge two given Gaussians without drifting from the real data distribution. An incrementally learning mixture model helps to reduce the complexity of the model while still giving a precise description of the observations. The results show that the proposed method generally achieves comparable performance to the conventional approach, while producing models at lower training time without any compromise with the model accuracy.