Bayesian ensemble learning for image denoising

TL;DR

This paper introduces a Bayesian ensemble learning approach combining Non-local Means and Fields of Experts for image denoising, achieving about 1dB improvement over individual methods.

Contribution

It presents a novel Bayesian ensemble framework that integrates two advanced denoising algorithms to enhance image restoration quality.

Findings

Ensemble method improves denoising performance by nearly 1dB.

Bayesian ensemble effectively combines strengths of Non-local Means and Fields of Experts.

Experimental results demonstrate superior results compared to single algorithms.

Abstract

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.