模糊推理最大熵图像恢复算法

Maximum Entropy Image Restoration Algorithm Using Fuzzy Inference System

与线性恢复算法相比,基于最大熵的图像恢复算法具有更好的图像恢复效果,但其收敛速度较慢。为了提高最大熵图像恢复算法的收敛速度,首先给出了算法的非周期反卷积模型,然后采用模糊推理系统在线确定算法的迭代步长。由于采用了可变步长,因此极大地提高了算法的收敛速度。仿真实验表明提出的算法收敛速度快,图像恢复效果好。

Comparing with the linear image restoration algorithms, the image restoration algorithm based on maximum entropy can obtain better performance. However, it has slow convergence rate. To improve the convergence speed of the maximum entropy based image restoration algorithm, we firstly present the aperiodic model of deconvolution, and then a fuzzy inference system is introduced to determine the iterative step size online. Since we adopt a variable step size, the convergence speed is significantly improved. The computer simulation results show that the proposed algorithm for image restoration has a faster convergence speed and yields improved restoration performance.