Abstract: For effective edge feature preservation and good denoising result, this paper proposes a new adaptive fidelity term total variation image denoising algorithm based on bidimensional local mean decomposition and local high-frequency energy. Firstly, an image is decomposed into different scale parts from low frequency to high frequency through using bidimensional local mean decomposition algorithm; secondly, the highest frequency component can be used to calculate the local energy function, and then the adaptive fidelity term is achieved; finally, image denoising can be performed by minimizing the energy functional. This paper implements a parameter sensitive analysis for adaptive fidelity term total variation image denoising algorithm to verify its high uncertainty. In comparison with other image denoising algorithms, our algorithm presents a better result in edge feature preservation and noise removing under strong noise. Our algorithm solves some problems existing in other denoising algorithms, e.g., staircase effect, speckle effect and poor denoising results near the edge. The experimental results approve that our algorithm has a better robustness and more faster processing in comparison with the adaptive fidelity term total variation image denosing algorithm.