-
针对传统鬼成像目标重建算法在低测量次数下重建质量较差的问题，本文提出了一种结合盲噪声估计与迭代滤波降噪的目标重建方法，旨在优化低测量数据条件下的鬼成像效果，提升目标重建质量。为解决噪声和欠采样带来的不准确重建，首先通过伪逆运算和单位矩阵提高测量矩阵的稳定性，然后计算修正项以优化桶探测器的观测数据。使用平衡的全一列向量作为初始值，以加速收敛。在迭代过程中，引入一种新的目标图像降噪算法，该算法结合了盲噪声估计、块匹配三维滤波和导向滤波。这种动态滤波有效保留了每次迭代中的重要细节，即便在测量次数较低的情况下，仍能实现高质量的目标重建。仿真和实验结果表明，该方法在边缘保留和纹理细节质量上优于传统方法，为鬼成像技术在遥感和医学成像等领域的应用提供重要的技术支持。This paper introduces an Adaptive Blind Noise Dynamic Filtering for Ghost Imaging Reconstruction (ABNDF-GIR), a novel method designed to optimize ghost imaging data with limited measurement numbers, significantly improving image quality and peak signal-to-noise ratio (PSNR). To address the challenges of noise and undersampling, we first enhance the stability of the measurement matrix using pseudoinversion and a unit matrix, and correction terms are calculated for bucket detector observations to refine the reconstruction process. A balanced all-one column vector is employed as the initial value to accelerate convergence. For iterative computation, we propose a novel filtering and denoising technique, the Adaptive Denoising Window-Based Guided Filtering with BM3D (ADW-BG), which integrates blind noise estimation, Block Matching and 3D Filtering, and guided filtering. This dynamic filtering method effectively preserves important details during each iteration, enabling high-quality target reconstruction even with fewer measurements. Extensive simulations and experimental results confirm that our method significantly outperforms traditional filtering approaches and various compressed sensing algorithms, especially in edge preservation and texture detail enhancement. The proposed technique offers crucial technical advancements for the application of ghost imaging in fields such as remote sensing and medical imaging, showing clear advantages in real-world imaging scenarios.
-
Keywords:
- Ghost Imaging /
- Blind Noise Estimation /
- Iterative Operation /
- Low Measurement Number
-
[1] Klyshko D N 1988 Zh. Eksp. Teor. Fiz. 94 82
[2] Pittman T B, Shih Y, Strekalov D, Sergienko A V 1995 Phys. Rev. A 52 R3429
[3] Cheng J, Han S 2004Phys. Rev. Lett. 92 093903
[4] Cao D Z, Xiong J, Wang K 2005Phys. Rev. A 71 013801
[5] Zhao C, Gong W, Chen M, Li E, Wang H, Xu W, Han S 2012Appl. Phys. Lett. 101 141123
[6] Deng C, Gong W, Han S 2016 Opt. Express 24 25983
[7] Wang C, Mei X, Pan L, Wang P, Li W, Gao X, Bo Z, Chen M, Gong W, Han S 2018 Remote Sens. 10 732
[8] Huang X, Xu Y, Bai Y, Fu X 2023 Opt. Lett. 48 5543
[9] Zhang L, Wang X, Zhou Q, Xue J, Xu B 2024 Opt. Express 32 4242
[10] Ma J, Li Z, Zhao S, Wang L 2023 Opt. Express 31 11717
[11] Huang W, Tan W, Qin H, Wang J, Huang Z, Huang X, Fu X, Bai Y 2023 J. Opt. Soc. Am. B 40 1696
[12] Wang Y, Wang X, Gao C, Yu Z, Wang H, Zhao H, Yao Z 2024 Sensors 24 4197
[13] Ferri F, Magatti D, Lugiato L, Gatti A 2010 Phys. Rev. Lett. 104 253603
[14] Sun B, Welsh S S, Edgar M P, Shapiro J H, Padgett M J 2012 Opt. Express 20 16892
[15] Li M, Zhang Y, Luo K, Wu L, Fan H 2013 Phys. Rev. A 87 033813
[16] Zhang J, Zhao D, Li Y, Liu Y, Sun M, Li X, Yu Z, Zhou X 2023 Appl. Opt. 62 7678
[17] Bai X, Li Y Q, Zhao S M 2013 Acta Phys. Sin. 62 044209(in Chinese) [白旭, 李永强, 赵生妹2013物理学报62 044209]
[18] Wang L, Zhao S 2020 Chin. Phys. B 29 024204
[19] Zhang C, Guo S, Cao J, Guan J, Gao F 2014 Opt. Express 22 30063
[20] Gong W 2015 Photonics Res. 3 234
[21] Yao X, Yu W, Liu X, Li L, Li M, Wu L, Zhai G 2014 Opt. Express 22 24268
[22] Li G, Yang Z, Zhao Y, Yan R, Liu X, Liu B 2017 Laser Phys. Lett. 14 025207
[23] Zhang H, Zhao C, Ju X, Tang J, Xiao T 2022Acta Phys. Sin. 71 074201(in Chinese) [张海鹏，赵昌哲，鞠晓璐，汤杰，肖体2022物理学报71 074201]
[24] Chen L, Wang C, Xiao X, Ren C, Zhang D, Li Z, Cao D 2022Opt. Express 306248
[25] Wu H, Wang C, Gong W 2018 Opt. Express 26 4183
[26] Xu C, Li D, Fan X, Lin B, Guo K, Yin Z, Guo Z 2023 Phys. Scr. 98 065011
[27] Kataoka S, Mizutani Y, Uenohara T, Takaya Y, Matoba O 2022 Appl. Opt. 61 10126
[28] Zhou C, Liu X, Feng Y, Li X, Wang G, Sun H, Huang H, Song L 2022 Opt. Lasers Eng. 156 107101
[29] Fan Y, Bai Y, Fu Q, Zhang R, Zhou L, Zhu X, Zou X, Fu X 2024 Opt. Commun. 566 130684
[30] Katkovnik V, Astola J 2012 J. Opt. Soc. Am. A 29 1556
[31] Zhou C, Feng D, Wang G, Huang J, Huang H, Liu X, Li X, Feng Y, Sun H, Song L 2023 Opt. Express 31 25013
[32] Tomasi C, Manduchi R 1998Proceedings of the Sixth International Conference on Computer Vision Bombay, India, January 7, 1998 p839
[33] Buades A, Coll B, Morel J-M 2005 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition San Diego, CA, USA, June 20-25, 2005 p60
[34] Dabov K, Foi A, Katkovnik V, Egiazarian K 2007 IEEE Trans. Image Process.16 2080
[35] He K, Sun J, Tang X 2012 IEEE Trans. Pattern Anal. Mach. Intell. 35 1397
[36] Liu X, Jing X-Y, Tang G, Wu F, Ge Q 2017 Signal Process. 135 239
[37] Izadi S, Sutton D, Hamarneh G 2023 Artif. Intell. Rev. 56 5929
[38] Liu X, Tanaka M, Okutomi M 2013 IEEE Trans. Image Process. 22 5226
[39] Lei T, Zhang R, Ma Y, Ding X, Wu Y, Shiyong W 2024 Opt. Commun. 550 130023
[40] Li L, Yao X, Liu X, Yu W, Zhai G 2014Acta Phys. Sin. 63 224201(in Chinese) [李龙珍，姚旭日，刘雪峰，俞文凯，翟光杰2014物理学报63 224201]
计量
- 文章访问数: 16
- PDF下载量: 0
- 被引次数: 0
下载:
