双向增强扩散滤波的图像去噪模型

提出一种双向增强扩散滤波的图像去噪模型。简化扩散方程建立双向扩散系数，使模型在扩散过程中能够实现平滑与锐化的双向过程，为加强平滑和锐化强度，用小波变换增强图像，使整体图像轮廓得到增强和局部图像纹理特征得到弱化。然后，对阈值进行了自适应设计和改进，使其根据图像的最大灰度值和迭代次数自动控制阈值，进一步保留图像边缘和细节特征。实验仿真和可行性的验证结果表明，新模型去噪效果较理想，不但能抑制噪声，而且能保护细节信息，峰值信噪比得到了有效的提高，性能更优越。

Image De-noising Model based on Bidirectional Enhanced Diffusion Filter

A bidirectional enhanced diffusion filter image de-noising model is presented. The diffusion equation is firstly simplified and analyzed to establish bidirectional diffusion coefficient. Hence, the two way process of smoothing and sharpening can be achieved by the model in the diffusion process, To further enhance the strength of the smoothing and sharpening, image enhancement is used to enhance the overall outline of the image using wavelet transform, thus weakening texture detail of the image. Then, the threshold will be designed and improved, and it will be automatically controlled by maximum image gray value and iterative times, which can retain the image edge and detail features. The proposed model is be simulated. The experimental result shows that the new model is ideal, and it can improve the performance of de-noising and the protection of edge. The texture detail information is satisfactory. The peak signal to noise ratio is promoted drastically. Therefore,the performance is better than classical algorithms.