提出一种消除阶梯效应与增强细节的变分Retinex红外图像增强新算法。将高斯曲率正则项应用到变分Retinex模型的构建中，采用一阶微分对模型添加细节增强约束项，实现细节信息的自适应增强。结合邻域差分，引入曲率滤波方法对变分模型求取最优解。实验结果表明，与采用其他变分Retinex方法相比，采用新算法处理后的图像在定量评价指标上优于对比算法。新算法能消除阶梯效应，增强图像的细节和视觉效果。

One novel variational Retinex algorithm for infrared image enhancement is proposed to eliminate the staircase effect and enhance details. Gaussian curvature regularization is adopted to construct the variational Retinex model, and the first-order differential term is adopted to add the detail enhancement constraint, which realizes the adaptive enhancement of detail information. In combination with the neighborhood difference operation, the curvature filter solver for variational model is introduced to achieve the optimal solution. The experimental results demonstrate that, compared with those processed by the other variational Retinex algorithms, the quantitative evaluation indexes of images processed by the proposed novel algorithm are superior. The new algorithm can eliminate the staircase effect and enhance the details and visual effect of images.