基于预分类的非局部被动毫米波图像去噪算法

针对被动毫米波图像的分辨率低,不具备丰富的纹理信息的特点,提出了一种基于预分类的非局部被动毫米波图像去噪算法(PBNL).利用图像梯度信息的奇异值分解获取图像的局部区域特征,根据不同的特征集将图像进行分类,并对不同的类别采取不同的去噪算法.实验结果表明,相对于非局部均值(NL-Means)算法,该方法在计算(时间)复杂度上有了明显的降低,降噪结果的PSNR值优于BM3D、各向异性去噪算法,并且在视觉上获得了更好的辨识效果. 

Pre-Selection Based Non-Local Passive Millimeter Wave Image Denoising Algorithm

Given that the low resolution and poor texture information of passive millimeter wave images, a pre-selection based image de-noising algorithm(PBNL) was proposed in this paper. The local regional characteristics of the image were obtained by using the singular value decomposition(SVD) of image gradient information, then the image was divided into different categories, and relevant algorithms were adopted. Experimental results show that, in contrast to the non local means(NL-Means) algorithm, the computational time complexity of the method proposed in this paper is significantly reduced and peak signal-to-noise ratio(PSNR) is superior to current state-of-art denoising algorithms, such as BM3D, anisotropic denoising algorithm, and it can get a better recognition results visually.