基于变分模型的梯度域色阶映射算法

针对梯度域动态范围压缩算法存在伪边缘以及局部细节扭曲等现象，提出了一种基于变分模型的梯度域色阶映射方法。首先，在梯度域内构造了一个既能够压缩图像的动态范围又能够保证边缘与细节信息的变分模型；其次，将Gibbs采样的思想引入到最速下降法，在求解变分模型最优解的同时有效地提高了无约束最优算法的收敛速度；最后通过改进的最速下降法得到变分模型最优解。实验结果表明该算法能够有效地去除光晕，得到细节保持完好的低动态范围图像。另外，改进的最速下降法保证了算法的实时性。

Tone mapping based on variational model in gradient domain

Due to the low dynamic range image which is generated by the gradient domain high dynamic range compression algorithm contain the artificial boundaries and local detail distortions, a variational model in gradient domain is proposed to improve the performance of the traditional algorithm. First of all, a variational model in gradient domain was introduced to compress dynamic range, meanwhile details and edges were preserved simultaneously. Afterwards, the rate of convergence is improved by introducing the ideology of Gibbs sampler. Eventually, the improved method was employed to obtain the optimal solution of the variational model. Experimental results demonstrate that proposed algorithm reduce the degree of artificial boundaries, meanwhile low dynamic range image represents excellent capacity of detail preservation. Moreover, the real-time performance is guaranteed by the improved steepest descent method.