L-R Imaging Algorithm for Passive Millimeter Wave Based on Sparse Representation
-
摘要: 在被动毫米波的图像恢复中，L-R算法是一种简单而有效的非线性方法。但当噪声不可忽略时，L-R算法难以获得较好的复原结果。自适应稀疏表示，作为一种新的信号处理方法，具有表达信号灵活的特点，能够在保持目标特征的同时有效地去除噪声。该文提出一种基于自适应稀疏表示的L-R算法。首先采用稀疏信号表示的方法进行去噪，然后使用L-R算法进行图像恢复。这种改进算法通过使用基于自适应稀疏表示的去噪算法有效地减少了噪声对L-R算法的影响。实验数据的成像结果表明：该文的改进算法提高了L-R算法的性能，可用于低信噪比的图像复原。Abstract: In passive millimeter wave image restoration, L-R algorithm is a simple and effective nonlinear method. However, when the noise can not be neglected, it is difficult for L-R algorithm to get good restoration. As a novel signal processing method, adaptive sparse representation has a merit of representing signal flexibly and can de-noise effectively when maintaining features of targets. A novel L-R algorithm is proposed based on adaptive sparse representation. It first de-noises by employing sparse signal representation, and then restores images by using L-R algorithm. The modified algorithm reduces the influence of noise on L-R algorithm effectively by using de-noise algorithm based on adaptive sparse representation. The imaging results of experiment data show that the modified algorithm proposed in the paper improves the performance of L-R algorithm, and it can be used in image restoration when the signal to noise ratio is low.
-
Pirogov Y A, Gladun V V, and Tischenko D A, et al.. Passive millimeter-wave imaging with superresolution[C]Image and Signal Processing for Remote Sensing X, Proc. of SPIE, Bellingham, WA, 2004, 5573: 72-83. [2] Lettington A H, Yallop M R, and Dunn D. Review of super-resolution techniques for passive millimeter-wave imaging[C]. Infrared and Passive Millimeter-wave Systems: Design, Analysis, Modeling, and Testing, Proc. of SPIE, Bellingham, USA, 2002, 4719: 230-239. [3] Dupe F X, Fadili J M, and Starck J L. A proximal iteration for deconvolving Poisson noisy images using sparse representations[J].. IEEE Transactions on Image Processing.2009, 18(2):310-321 -
计量
- 文章访问数: 3353
- HTML全文浏览量: 59
- PDF下载量: 1221
- 被引次数: 0
下载:
