Laser speckle reduction based on compressive sensing and edge detection

Dong-hai Wen; Yue-song Jiang; Hou-qiang Hua; Rong Yu; Qian Gao; Yan-zhong Zhang

doi:10.1117/12.2031693

19 September 2013 Laser speckle reduction based on compressive sensing and edge detection

Dong-hai Wen, Yue-song Jiang, Hou-qiang Hua, Rong Yu, Qian Gao, Yan-zhong Zhang

Proceedings Volume 8905, International Symposium on Photoelectronic Detection and Imaging 2013: Laser Sensing and Imaging and Applications; 890506 (2013) https://doi.org/10.1117/12.2031693
Event: ISPDI 2013 - Fifth International Symposium on Photoelectronic Detection and Imaging, 2013, Beijing, China

Abstract

Polarization active imager technology obtains images encoded by parameters different than just the reflectivity and therefore provides new information on the image. So polarization active imager systems represent a very powerful observation tool. However, automatic interpretation of the information contained in the reflected intensity of the polarization active image data is extremely difficult because of the speckle phenomenon. An approach for speckle reduction of polarization active image based on the concepts of compressive sensing (CS) theory and edge detection. First, A Canny operator is first utilized to detect and remove edges from the polarization active image. Then, a dictionary learning algorithm which is applied to sparse image representation. The dictionary learning problem is expressed as a box-constrained quadratic program and a fast projected gradient method is introduced to solve it. The Gradient Projection for Square Reconstruction (GPSR) algorithm for solving bound constrained quadratic programming to reduce the speckle noise in the polarization active images. The block-matching 3-D (BM3D) algorithm is used to reduce speckle nosie, it works in two steps: The first one uses hard thresholding to build a relatively clean image for estimating statistics, while the second one performs the actual denoising through empirical Wiener filtering in the transform domain. Finally, the removed edges are added to the reconstructed image. Experimental results show that the visual quality and evaluation indexes outperform the other methods with no edge preservation. The proposed algorithm effectively realizes both despeckling and edge preservation and reaches the state-of-the-art performance.

Citation Download Citation

Dong-hai Wen, Yue-song Jiang, Hou-qiang Hua, Rong Yu, Qian Gao, and Yan-zhong Zhang "Laser speckle reduction based on compressive sensing and edge detection", Proc. SPIE 8905, International Symposium on Photoelectronic Detection and Imaging 2013: Laser Sensing and Imaging and Applications, 890506 (19 September 2013); https://doi.org/10.1117/12.2031693

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