A Mixture Autoregressive Image Generative Model on Quadtree Regions for Gaussian Noise Removal via Variational Bayes and Gradient Methods
For researchers in image denoising, this work presents a novel probabilistic framework with analytical gradient updates, but the lack of quantitative results limits its immediate impact.
This paper proposes a probabilistic image generative model combining quadtree partitioning with a mixture autoregressive model for grayscale image denoising, and develops a variational Bayes and gradient-based algorithm that reduces MAP estimation to maximizing a variational lower bound. Experiments show the algorithm removes noise, but no quantitative results are provided.
This paper addresses the problem of image denoising for grayscale images. We propose a probabilistic image generative model that combines a quadtree region-partitioning model with a mixture autoregressive model, and propose a framework that reduces MAP (maximum a posteriori)-estimation-based denoising to the maximization of a variational lower bound. To maximize this lower bound, we develop an algorithm that alternately applies variational Bayes and gradient methods. We particularly demonstrate that the gradient-based update rule can be computed analytically without numerical computation or approximation. We carried out some experiments to verify that the proposed algorithm actually removes image noise and to identify directions for future improvement.