基于改进型正则化算法的图像融合优化研究

Research on image fusion optimization based on improved regularization algorithm

为了提高激光雷达融合图像的图像质量,设计了一种改进型正则化算法。该算法将稀疏表示加权编码以参数形式引入正则化算法,用于合理分配抑制噪声类型的权重以及提高算法鲁棒性。推导了包含迭代系数h与正则项参数λ的目标函数,给出了算法实现的具体流程步骤。实验对包含散粒噪声的融合图像进行优化,结果显示,随着h的增大图像质量提高,当h达到40后图像质量趋于稳定。当λ∈(0,0.5),随着λ的增大图像质量提高,当λ∈(0.5,1.0),随着λ的增大,图像质量下降。由此可见,本算法在h=40,λ=0.5时达到最优解,并且其时效性优于传统算法。由此可见,本算法在融合图像的图像质量增强方面具有一定的应用价值。

In order to improve the image quality of lidar fusion images, an improved regularization algorithm was designed.The algorithm introduces sparse representation weighted coding into the regularization algorithm in the form of parameters, which is used to reasonably assign the weights of noise suppression types and improve the robustness of the algorithm.The objective function including iteration coefficient h and regular term parameter λ is deduced, and the specific process steps of algorithm implementation are given.The experiment optimizes the fusion image containing shot noise.The results show that as h increases, the image quality improves, and when h reaches 40,the image quality tends to be stable.When λ∈(0,0.5),the image quality increases as the increases of λ,and when λ∈(0.5,1.0),the image quality decreases as the increases of λ.It can be seen that this algorithm achieves the optimal solution when h=40,λ=0.5,and its timeliness is better than the traditional algorithm.It can be seen that this algorithm has certain application value in enhancing the image quality of fused images.