基于RBF 神经网络的低对比度图像自适应增强算法

针对低对比度图像增强问题，提出了一种将直方图修正与RBF 神经网络相结合的图像对比度增强算法。首先由原始图像获得与其邻域存在对比度的像素的条件概率直方图，通过调整两个增强参数可以改变条件概率直方图和均匀分布直方图的权重，生成新的直方图对图像进行增强。采用RBF 神经网络建立图像特征与两个增强参数之间的非线性映射关系。根据图像本身的特征快速获得增强参数，从而实现图像的自适应增强。该方法计算量小，实时性强，应用范围广，有较强的自适应性。

Adaptive Low Contrast Image Enhancement Algorithm Based on the RBFNeural Network

For low-contrast image enhancement problem, we propose an algorithm based on histogramcorrection and RBF neural network methods. Obtained the conditional probability histogram of thepixels in the presence of contrast with its neighborhood through original image, adjusting the weightsof two parameters can change the conditional probability histogram and uniform distributionhistogram. In this paper, RBF neural network is applied to set up the nonlinear mapping betweenimage features and two enhanced parameters. In order to achieve adaptive image enhancement, rapidenhancement parameters are obtained according to the characteristics of the original image. Theresults show this method has good real-time ability, wide range of application, low computationalcomplexity and good adaptability.