This study presents a computational ghost imaging (GI) scheme that utilizes sequenced random speckle pattern illumination. The primary objective is to develop a speckle pattern/sequence that improves computational time without compromising image quality. To achieve this, we modulate the sequence of speckle sizes and design experiments based on three sequence rules for ordering the random speckle patterns. Through theoretical analysis and experimental validation, we demonstrate that our proposed scheme achieves a significantly better contrast-to-noise rate (CNR) compared to traditional GI at a similar resolution. Notably, the sequential GI method outperforms conventional approaches by providing over 10 times faster computational speed in certain speckle composition groups. Furthermore, we identify the corresponding speckle sizes that yield superior image quality, which are found to be geometrically proportional to the reference object area. This innovative approach utilizing sequenced random speckle patterns demonstrates potential suitability for imaging objects with complex or unknown shapes. The findings of this study hold great promise for advancing the field of computational GI and pseudo-thermal GI, addressing the need for improved computational efficiency while maintaining high-quality imaging.

In this study, the influence of hybrid speckle patterns on the contrast-to-noise ratio (CNR) and resolution in pseudo-thermal ghost imaging (PGI) was examined based on the object dimensions in the macroscopic and microscopic regimes. This research shows that an enhanced scaling of the ghost image CNR and resolution from that of the hybrid speckle pattern was observed with the increase in speckle size for a macroscopic object, compared with the use of single-size speckle patterns. For microscopic objects, the hybrid speckle pattern also offered the advantage of retrieving ghost images even if the CNR followed the same trend as the resolution. These results were verified using two different slits with the same transmitted area. In addition, the numerical analysis revealed that the interference of the hybrid speckle pattern was the major factor for a better CNR. Based on these findings, the novel hybrid speckle pattern found in this research provides a possible way for future experiments in PGI to regulate hybrid speckle patterns to obtain a better ghost image quality.

This study presents a novel approach for achieving high-quality and large-scale microscopic ghost imaging by integrating deep learning-based denoising with computational ghost imaging techniques. By utilizing sequenced random speckle patterns of optimized sizes, we reconstructed large noisy images with fewer patterns while successfully resolving fine details as small as 2.2 μm on a USAF resolution target. To enhance image quality, we incorporated the Deep Neural Network-based Noise2Void (N2V) model, which effectively denoises ghost images without requiring a reference image or a large dataset. By applying the N2V model to a single noisy ghost image, we achieved significant noise reduction, leading to high-resolution and high-quality reconstructions with low computational resources. This method resulted in an average Structural Similarity Index (SSIM) improvement of over 324% and a resolution enhancement exceeding 33% across various target images. The proposed approach proves highly effective in enhancing the clarity and structural integrity of even very low-quality ghost images, paving the way for more efficient and practical implementations of ghost imaging in microscopic applications.