Abstract
At present, there are many models for denoising images, such as the Perona - Malik model (PM) and the Gaussian filtering model that we often mention. These models can achieve good denoising effects in experiments, but in actual research, they are all imperfect. Based on this, the effective model for image denoising is proposed, which combines fractional-order fidelity and global gray degree fidelity. The combination of these two fidelity terms can measure the similarity of gray value changes between images, while it can remove the staircase effect and strengthen the texture information of the image. Experiments show that the model is superior to the model based on gradient fidelity in removing image noise.
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