Abstract-We propose an adaptive learning procedure to learn patch-based image priors for image denoising. The new algorithm, called the Expectation-Maximization (EM) adaptation, takes a generic prior learned from a generic external database and adapts it to the noisy image to generate a specific prior. Different from existing methods that combine internal and external statistics in ad-hoc ways, the proposed algorithm is rigorously derived from a Bayesian hyper-prior perspective. There are two contributions of this paper: First, we provide full derivation of the EM adaptation algorithm and demonstrate methods to improve the computational complexity. Second, in the absence of the latent clean image, we show how EM adaptation can be modified based on pre-filtering. Experimental results show that the proposed adaptation algorithm yields consistently better denoising results than the one without adaptation and is superior to several state-of-the-art algorithms.
Luo et al . REF proposed a prior learning algorithm that combines the internal and external priors from a Bayesian hyper-prior perspective.
Adaptive Image Denoising by Mixture Adaptation
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Mathematics Computer Science Medicine