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This paper introduces an adaptive blind noise dynamic filtering for ghost imaging reconstruction (ABNDF-GIR), a novel method of optimizing ghost imaging data with a limited number of measurements, 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 by using pseudoinversion and a unit matrix, and calculate correction terms for bucket detector observations to optimize the reconstruction process. A balanced all-one column vector is used 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, and can achieve high-quality target reconstruction even with fewer measurements. Extensive simulations and experimental results verify that our method is significantly superior to traditional filtering methods and various compressiv sensing algorithms, especially in edge preservation and texture detail enhancement. The proposed technique provides a key technical advancement for the application of ghost imaging in fields such as remote sensing and medical imaging, showing significant advantages in real-world imaging scenarios.
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Keywords:
- ghost imaging /
- blind noise estimation /
- iterative operation /
- low measurement number
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图 1 基于赝热光源的鬼成像原理示意图. BS表示分束棱镜, PC表示计算机, $ {z}_{1} $表示目标与光源之间的距离, $ {z}_{2} $表示CCD与光源之间的距离
Figure 1. Principle of ghost imaging using pseudo-thermal light source. BS represents the beam splitter; PC represents the computer, $ {z}_{1} $ denotes the distance between the target and the light source, $ {z}_{2} $ denotes the distance between the CCD and the light source.
图 2 ABNDF-GIR方法的示意图. 蓝色部分计算桶探测器观测值的修正项, 黄色部分涵盖了目标重建的迭代计算过程, 绿色部分展示了用于目标重建和噪声抑制的ADW-BG方法, 红色部为迭代停止准则
Figure 2. Schematic diagram of the ABNDF-GIR method. The blue section calculates correction terms for bucket detector observations, the yellow section covers the iterative computation process of compressed sensing ghost imaging, the green section presents the proposed ADW-BG method for target reconstruction and noise reduction, and the red section assesses the iterative stopping condition.
图 3 含噪图像噪声水平盲估计原理示意图
Figure 3. Schematic diagram of blind estimation for noise level in noisy image.
图 4 不同滤波方法的迭代重建“Airplane”二值图像结果 (a)为原始图像; (b)—(f)分别为采用BF, NLF, BM3D, GF和ADW-BG方法在迭代算法中得到的重建图像
Figure 4. Iterative reconstruction results of the “Airplane” binary image using different filtering methods: (a) Original image; (b)–(f) the reconstructed images obtained using BF, NLF, BM3D, GF, and ADW-BG methods, respectively, in the iterative algorithm.
图 5 不同滤波方法下“Dinosaur”灰度图像的迭代重建结果 (a)原始图像; (b)—(f)分别为采用BF, NLF, BM3D, GF和ADW-BG方法重建的图像
Figure 5. Iterative reconstruction results of the grayscale target “Dinosaur” using different filtering methods: (a) Original image; (b)–(f) the images reconstructed using the BF, NLF, BM3D, GF, and ADW-BG methods, respectively.
图 6 不同测量次数N下, 各滤波方法迭代重建结果的PSNR值比较
Figure 6. Comparison of PSNR values for reconstruction results using different filtering methods in iterative operations under varying measurement numbers N.
图 7 使用不同鬼成像目标重建方法的重建结果比较 (a), (a1), (a2)和(a3)分别展示了原始目标: “Double Slit”, “Expression”, “TOP”和“Car”; (b), (b1), (b2)和(b3)展示了使用OMP方法的重建结果; (c), (c1), (c2)和(c3)展示了使用TVAL3方法的重建结果; 图(d), (d1), (d2)和(d3)展示了本文方法的重建结果
Figure 7. Comparison of reconstruction results using different ghost imaging target reconstruction methods. Panels (a), (a1), (a2), and (a3) display the original targets: “Double Slit,” “Expression,” “TOP,” and “Car,” respectively. Panels (b), (b1), (b2), and (b3) present the reconstruction results using OMP; panels (c), (c1), (c2), and (c3) showcase the reconstruction results using TVAL3; and panels (d), (d1), (d2), and (d3) demonstrate the reconstruction results of our method.
图 8 不同重建算法在“Double Slit”图像第64行上的像素值对比曲线. “Original”表示原始目标图像的像素值曲线
Figure 8. Comparison curve of pixel values in the 64 th row of the “Double-Slit” image for different reconstruction algorithms. “Original” refers to the curve of pixel values for the original target.
图 9 不同重建方法在不同SNR条件下的PSNR值比较
Figure 9. Comparison of PSNR values of different reconstruction methods under various SNR conditions.
图 10 不同重建方法在不同测量次数下的重建目标可视化比较
Figure 10. Visualization comparison of reconstruction targets for different reconstruction methods at varying measurement numbers.
表 1 不同目标重建中不同滤波方法的PSNR值对比
Table 1. PSNR values for different filtering methods in the reconstruction of various targets.
BF
/dBNLF
/dBBM3D
/dBGF
/dBADW-BG(dB) “Airplane” 19.23 21.58 22.67 24.15 26.16 “Dinosaur” 25.69 25.90 25.98 26.42 27.56 
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表 2 不同方法的重建结果PSNR值
Table 2. Reconstruction results in PSNR for different methods.
OMP/dB TVAL3/dB ABNDF-GIR/dB (a) 21.78 15.89 34.12 (a1) 16.17 17.37 25.41 (a2) 18.25 17.63 30.95 (a3) 22.27 24.38 26.94
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