One of the fundamental problems in the field of image processing is denoising. The underlying goal of image denoising is to effectively suppress noise while keeping intact the significant features of the image, such as texture and edge information. The gradient of image is a famous feature descriptor in denoising models to distinguish edges and ramps. If the received signal of an image is very noisy, the gradient cannot effectively distinguish between the image edges and the image ramps. In this paper, based on the difference curvature and the gradient of the image, we introduce a new feature descriptor. For demonstrating the effectiveness of the new feature descriptor, we use it in constructing a new diffusion-based denoising model. Experimental results show the effectiveness of the method.

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