摘要： 在图像获取过程中，常得到含有噪声和对比度较差的图像，为更好地去除图像的噪声与增强对比度，提出一种基于矢量扩散控制的图像同步去噪增强方法。分析全变分(TV)模型的构造，指出其存在的问题，通过引入矢量扩散控制的方式改造该模型的后项，更好地控制扩散在图像边缘处的粒度。给出限制对比度自适应直方图均衡的微分模型结构，并与改进后的TV模型融合实现图像的同步去噪与反差增强。通过2组实验从成像质量和灰度分布上比较处理结果，验证该方法的有效性。实验结果表明，该方法不仅较好地解决了TV模型在去噪过程中出现的阶梯效应，而且能够改善图像对比度，提高图像的质量。

关键词: 去噪, 反差增强, 直方图均衡, 全变分模型, 限制对比度自适应直方图均衡模型, 矢量扩散控制

Abstract: In the process of image acquisition, the images which contain noise or poor contrast are often obtained. In order to realize image denoising and contrast enhancement, this paper presents a simultaneous denoising and enhancement method to process image based on vector diffusion control. The structure of Total Variation(TV) model is analyzed, the problem is pointed out, and edge intensity is better control by introducing the vector diffusion of the transformation model. The differential model of Contrast Limited Adaptive Histogram Equalization(CLAHE) is proposed, which is combined with the improved TV model to implement synchronization of image denoising and contrast enhancement. The results are comparing by the quality and gray distribution of two group experiments, and the effectiveness of this proposed method is verified. The results show that the method not only can resolve the staircase appearing in TV model during denoising process, but also can improve the contrast of image, so it can improve the quality of image.

Key words: denoising, contrast enhancement, Histogram Equalization(HE), Total Variation(TV) model, Contrast Limited Adaptive Histogram Equalization(CLAHE) model, vector diffusion control

中图分类号: 

• TP391