  Natural images are often affected by random noise and image denoising has
long been a central topic in Computer Vision. Many algorithms have been
introduced to remove the noise from the natural images, such as Gaussian,
Wiener filtering and wavelet thresholding. However, many of these algorithms
remove the fine edges and make them blur. Recently, many promising denoising
algorithms have been introduced such as Non-local Means, Fields of Experts, and
BM3D. In this paper, we explore Bayesian method of ensemble learning for image
denoising. Ensemble methods seek to combine multiple different algorithms to
retain the strengths of all methods and the weaknesses of none. Bayesian
ensemble models are Non-local Means and Fields of Experts, the very successful
recent algorithms. The Non-local Means presumes that the image contains an
extensive amount of self-similarity. The approach of the Fields of Experts
model extends traditional Markov Random Field model by learning potential
functions over extended pixel neighborhoods. The two models are implemented and
image denoising is performed on natural images. The experimental results
obtained are used to compare with the single algorithm and discuss the ensemble
learning and their approaches. Comparing to the results of Non-local Means and
Fields of Experts, Ensemble learning showed improvement nearly 1dB.
