Wang Wei, Zhang Caixia, Ng Michael K
Opt Express. 2020 Jun 22;28(13):18751-18777. doi: 10.1364/OE.28.018751.
The performance of contrast enhancement is degraded when input images are noisy. In this paper, we propose and develop a variational model for simultaneously image denoising and contrast enhancement. The idea is to propose a variational approach containing an energy functional to adjust the pixel values of an input image directly so that the resulting histogram can be redistributed to be uniform and the noise of the image can be removed. In the proposed model, a histogram equalization term is considered for image contrast enhancement, a total variational term is incorporate to remove the noise of the input image, and a fidelity term is added to keep the structure and the texture of the input image. The existence of the minimizer and the convergence of the proposed algorithm are studied and analyzed. Experimental results are presented to show the effectiveness of the proposed model compared with existing methods in terms of several measures: average local contrast, discrete entropy, structural similarity index, measure of enhancement, absolute measure of enhancement, and second derivative like measure of enhancement.
当输入图像存在噪声时，对比度增强的性能会下降。在本文中，我们提出并开发了一种用于同时进行图像去噪和对比度增强的变分模型。其思路是提出一种包含能量泛函的变分方法，直接调整输入图像的像素值，以使得到的直方图能够重新分布为均匀的，并且可以去除图像的噪声。在所提出的模型中，考虑了一个直方图均衡项用于图像对比度增强，纳入了一个全变分项以去除输入图像的噪声，并添加了一个保真项以保持输入图像的结构和纹理。研究并分析了所提算法极小值的存在性和收敛性。给出了实验结果，以表明所提模型与现有方法相比在几种度量标准下的有效性：平均局部对比度、离散熵、结构相似性指数、增强度量、绝对增强度量以及类似二阶导数的增强度量。
