中文摘要：

引入l0范数重建传统的变分约束模型，得到基于l0范数数据保真项的图像去雾霾算法模型（l0-l0）．该模型将l0范数作为正则项和数据保真项，充分利用l0范数稀疏性的优点，对光滑图像有效逼近的同时保持了图像的几何特征不被破坏．结合图像层分离，把降质的图像分为基层和细节层，在图像基层进行动态范围调整，细节层进行细节操作．由于l0范数不易求解，利用交替方向法将原问题转化为3个子问题，并分别对3个子问题进行求解．实验结果表明：相比于l0-l2图像修复的方法，该模型对图像增强更为有效，而且具有普遍适用性．

英文摘要：

Introduced the l0 fidelity term to rebuild the conventional models and developed a new model （10-10） ,the model makes l0 norm as the regularization term and the data fidelity term. It makes full use of the advantages of l0 norm to effectively approaching smooth images at the same time keeping the image geometric feature is not damaged. Combining with the image layer separation, the degraded image is divided into basic layer and detail layer. The basic layer for the dynamic range modification and detail layer for detail magnification. Furthermore, by applying alternating direction method of multipliers（ADMM） to solve the model, derived fast convergent iterative algorithm which was applicable for image enhancement. The experimental results show that. compared to l0 smoothing image restoration method, the model（l0-l2） is more effectively for image enhancement, get a better result and shows the universal applicability and effectiveness of the method.