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.

图像处理领域中的基本问题之一是降噪。图像降噪的基本目标是有效抑制噪声，同时保持图像的重要特征（如纹理和边缘信息）完整无损。图像的梯度是去噪模型中区分边缘和坡度的著名特征描述符。如果图像的接收信号非常嘈杂，则梯度不能有效地区分图像边缘和图像斜坡。在本文中，基于图像的曲率差和梯度，我们引入了一个新的特征描述符。为了证明新特征描述符的有效性，我们将其用于构造新的基于扩散的去噪模型。实验结果表明了该方法的有效性。