一种基于压缩感知的斑点噪声去除算法

A Speckle Denoising Method Based on Compressed Sensing

针对现有斑点噪声去除算法去噪效果不明显、噪声残留度高等问题,将压缩感知(Compressed Sensing,CS)理论结合到图像处理中,提出一种基于压缩感知的斑点噪声去除算法。提出了斑点噪声模型,利用CS理论建立去噪模型并归纳其优化问题。将问题的解决方案分为3个步骤:稀疏表示、更新字典与图像重构。利用改善的正交匹配追踪算法(OMP)与奇异值分解算法(K-SVD),获得去噪后的重建图像。最后与现有的常用去噪算法进行性能仿真对比实验,在视觉感受与峰值信噪比(PSNR)2项评价指标上,均表明该算法具有较好的斑点噪声去除效果。

Due to the bad channel environment and poor image sampling equipment,images are often contaminated by noise in the process of collection,transmission and processing.Speckle noise,which is difficult and complex to eliminate,is one of the common noise appearing in image processing. Considering that traditional denoising methods do not work satisfactorily in speckle noise reduction,a speckle denoising method based on compressed sensing is proposed,which combines the CS theory and DIP（ Digital Image Processing）.The first step is to set up the speckle noise model,then the denoising model is established according to the CS theory and the corresponding optimization problem is raised. This problem is divided into three parts： the sparse representation stage,the dictionary updating stage and the image restoration stage.A modified OMP（ Orthogonal Matching Pursuit） method and an improved K-SVD method are used to solve these problems and get the denoised image.At last,simulations are conducted to compare the CS method and traditional methods.It is shown that the CS-based speckle denoising method performs well in PSNR and can significantly enhance the visual quality of the image.