Bucket detectors, commonly used in ghost imaging systems, include charge coupled devices (CCDs), complementary metal oxide semiconductor (CMOS) sensors, silicon photocells, single-photon detectors, and perovskite detectors. However, these detectors often come with high costs, limiting the widespread application of ghost imaging. Therefore, it is important to explore low-cost detectors to make ghost imaging more accessible. In this paper, we propose using silicon solar panels as bucket detectors in ghost imaging systems and investigate the performance of image reconstruction in this low-cost configuration. Silicon solar panels offer advantages such as broad-spectrum sensitivity and high efficiency. Additionally, with the rapid development of silicon-based photonic integrated circuits, silicon solar panels now feature high integration, which allows for the collection, processing, and analysis of experimental data on a single compact device. This integration can substantially lower the manufacturing costs of bucket detectors. Our goal is to explore the potential of silicon solar panels as a cost-effective alternative to traditional detectors in ghost imaging systems and to evaluate their performance under various sampling conditions and reconstruction algorithms.

We propose a pseudo-inverse ghost imaging system using commercial silicon solar panels as bucket detectors. In this system, the object light is captured by the silicon solar panel and converted into a voltage signal, which serves as the measurement signal, also called the bucket signal. In the experiment, a series of binary random patterns, generated by a projector, are sequentially projected onto the object, which is placed close to the silicon solar panel. The output voltage signals are digitized by an oscilloscope and then transmitted to a computer for image reconstruction. To verify the feasibility of the proposal, we first confirm the linear relationship between the output voltages of the silicon solar panel and the number of illuminated pixels. The deviation between the experimental data and theoretical predictions is minimal. For comparison, we also perform ghost imaging experiments using a CCD as the bucket detector under similar conditions. Image quality is evaluated using standard metrics. To further enhance image quality and optimize the experimental setup, various reconstruction algorithms are applied, including the correlation algorithm, pseudo-inverse algorithm, Schmidt orthogonalization, compressed sensing, and filtering techniques. The performance of ghost imaging with these methods is compared under different sampling rates.

The verification of the linear relationship between the voltage signals from the silicon solar panel and the illuminated area demonstrates the feasibility of the proposed experimental setup (Fig. 2). Following this, 4096 speckle patterns are generated, and the object “T” is imaged. To evaluate the performance of silicon solar panels as bucket detectors, we conduct several experiments at different sampling rates and reconstruction algorithms. Similar experiments with a CCD as the bucket detector are also conducted for comparison. The experimental results are shown in Figs. 3 and 4. When the sampling rate exceeds 40%, the object’s outline becomes more distinguishable, and the results from the silicon solar panel closely match those obtained from CCD detectors. However, the silicon solar panel offers a significant cost advantage, being much more affordable than CCDs. Fig. 5 presents the peak signal-to-noise ratio (PSNR) values of the reconstructed images under various sampling rates and reconstruction algorithms. The results indicate that, in comparison to CCDs, the PSNR values for images reconstructed using silicon solar panels remain stable across different conditions. This stability highlights the robustness of the system, with the silicon solar panels demonstrating superior fault tolerance and ease in selecting an optimal sampling rate. As a result, the system can maintain high-quality image reconstruction even under variable conditions or environments. Furthermore, Fig. 6 shows the structural similarity index (SSIM) values for the reconstructed images under different sampling rates and algorithms. The SSIM results corroborate the findings from the PSNR analysis, demonstrating that the use of silicon solar panels as detectors leads to stable image recovery, even under low sampling conditions. This suggests that silicon solar panels can provide reliable and high-quality imaging performance in resource-constrained environments, making them an attractive option for practical ghost imaging applications.

We propose a pseudo-inverse ghost imaging system based on silicon solar panels as bucket detectors. The low-cost silicon solar panels deliver good performance in reconstructing images, demonstrating their potential as viable alternatives to traditional, more expensive detectors like CCDs. Various reconstruction algorithms such as the pseudo-inverse, Schmidt orthogonalization, and quadratic filtering techniques are applied, and the results show that high-quality, stable images can be obtained even with low sampling rates. The silicon solar panels’ excellent photovoltaic properties, broad spectral response, and low cost have made them widely used in fields such as environmental monitoring, intelligent transportation, building surveillance, security, disaster monitoring, and aerospace. Our imaging system not only broadens the application scope of ghost imaging but also integrates silicon solar panels into cost-effective, high-efficiency detection and monitoring technologies, paving the way for more practical applications in real-world scenarios.