基于改进加权核范数最小化的图像去噪算法研究

Image Denoising Based on Improved Weighted Nuclear Norm Minimization

针对彩色图像的高斯噪声,在加权核范数最小化(WNNM)框架下,提出了一种对于彩色图像去噪的多信道(MC)优化模式。首先,选择多种类型噪声计算量,利用信道的冗余特性将RGB补丁连接起来,再引入权重矩阵以协调3种信道的图像保真性。把所提的MC-WNNM模型转换成线性等式约束现象,并采用交替位置乘子法(ADMM)解决。每个变量更新步骤都具有其封闭解,并能保证收敛性。基于真实用于无人机目标识别的彩色图像添加噪声进行仿真实验,实验结果说明,该算法相较现有的BM3D方法及WNNM方法具有明显优势。

Aiming at removal of Gaussian noise from color images a Multi-Channel(MC)optimization model for color image denoising is proposed in the framework of Weighted Nuclear Norm Minimization(WNNM).First multiple types of noise computation are selected RGB patches are connected by using the redundancy property of the channels.Then a weight matrix is introduced to reconcile the image fidelity of the three channels.The given MC-WNNM model is converted into a linear equation constrained phenomenon and is solved by using the Alternating Direction Method of Multipliers(ADMM).Each variable update step has its own closed solution and convergence is guaranteed.Simulation experiments based on real color images for UAV target identification with added noise show that the method has significant advantages over the existing BM3D and WNNM methods.