Image Transmission through Scattering Media Using Ptychographic Iterative Engine

Deep learning based coherent diffraction imaging of dynamic scattering media

The ptychographic iterative engine (PIE) is a lensless coherent diffraction imaging algorithm known for its simplicity, easy to use, scalability, and fast convergence. However, practical applications often encounter interference in imaging results caused by non-static scattering media, such as dense fog, seawater target detection and medical biology diagnosis. To address this challenge, we propose a novel approach using computational deep learning for dynamic scattering medium image reconstruction, enabling lens-free coherent diffraction imaging through dynamic scattering media. Through extensive analysis, we evaluate the effectiveness of the neural network for PIE image recovery under varying scattering medium concentration conditions. We also test scattering images obtained by hybrid training with different concentrations of scattering medium to assess the generalisation ability of the neural network. The experimental results demonstrate that our proposed method achieve PIE lens-free imaging under non-static scattering media interference. This coherent diffraction imaging method, based on transmission through dynamic scattering media, opens up new possibilities for practical applications of PIE and fosters its development in complex environments. Its significance extends to fields like atmospheric pollution, seawater target detection and medical biology diagnosis, providing valuable references for research in these domains.

Extrapolated speckle-correlation imaging with an untrained deep neural network

We present a method for speckle-correlation imaging with an extended field of view to observe spatially non-sparse objects. In speckle-correlation imaging, an object is recovered from a non-invasively captured image through a scattering medium by assuming shift-invariance of the optical process called the memory effect. The field of view of speckle-correlation imaging is limited by the size of the memory effect, and it can be extended by extrapolating the speckle correlation in the reconstruction process. However, spatially sparse objects are assumed in the inversion process because of its severe ill-posedness. To address this issue, we introduce a deep image prior, which regularizes the image statistics by using the structure of an untrained convolutional neural network, to speckle-correlation imaging. We experimentally demonstrated the proposed method and showed the possibility of extending the method to imaging through scattering media.

Optical ptychography for biomedical imaging: recent progress and future directions [Invited]

Ptychography is an enabling microscopy technique for both fundamental and applied sciences. In the past decade, it has become an indispensable imaging tool in most X-ray synchrotrons and national laboratories worldwide. However, ptychography's limited resolution and throughput in the visible light regime have prevented its wide adoption in biomedical research. Recent developments in this technique have resolved these issues and offer turnkey solutions for high-throughput optical imaging with minimum hardware modifications. The demonstrated imaging throughput is now greater than that of a high-end whole slide scanner. In this review, we discuss the basic principle of ptychography and summarize the main milestones of its development. Different ptychographic implementations are categorized into four groups based on their lensless/lens-based configurations and coded-illumination/coded-detection operations. We also highlight the related biomedical applications, including digital pathology, drug screening, urinalysis, blood analysis, cytometric analysis, rare cell screening, cell culture monitoring, cell and tissue imaging in 2D and 3D, polarimetric analysis, among others. Ptychography for high-throughput optical imaging, currently in its early stages, will continue to improve in performance and expand in its applications. We conclude this review article by pointing out several directions for its future development.

Non-line-of-sight imaging under white-light illumination: a two-step deep learning approach

Non-line-of-sight (NLOS) imaging has received considerable attentions for its ability to recover occluded objects from an indirect view. Various NLOS imaging techniques have been demonstrated recently. Here, we propose a white-light NLOS imaging method that is equipped only with an ordinary camera, and not necessary to operate under active coherent illumination as in other existing NLOS systems. The central idea is to incorporate speckle correlation-based model into a deep neural network (DNN), and form a two-step DNN strategy that endeavors to learn the optimization of the scattered pattern autocorrelation and object image reconstruction, respectively. Optical experiments are carried out to demonstrate the proposed method.

Single-shot sequential projection phase retrieval and 3D localization from chromatic aberration

A phase retrieval method based on sequential projection and chromatic aberration is reported. Construction of this method includes a red, green and blue (RGB) LED, an objective and a color camera. Owing to the chromatic aberration characteristics of the objective, three color images obtained by the color camera correspond to three equivalent propagation planes. Equivalent relative distances among these planes can be obtained by defining and iteratively minimizing the convergence index. Then, sequential projection strategy is used for phase retrieval in each image plane. Based on the recovered phase information and angular spectrum propagation principle, digital refocusing and 3D localization can be achieved for each subregion of the sample. Finally, the feasibility of our method is demonstrated by simulations and experiments.

Imaging Correlography Using Ptychography

Imaging correlography, an effective method for long-distance imaging, recovers an object using only the knowledge of the Fourier modulus, without needing phase information. It is not sensitive to atmospheric turbulence or optical imperfections. However, the unreliability of traditional phase retrieval algorithms in imaging correlography has hindered their development. In this work, we join imaging correlography and ptychography together to overcome such obstacles. Instead of detecting the whole object, the object is measured part-by-part with a probe moving in a ptychographic way. A flexible optimization framework is proposed to reconstruct the object rapidly and reliably within a few iterations. In addition, novel image space denoising regularization is plugged into the loss function to reduce the effects of input noise and improve the perceptual quality of the recovered image. Experiments demonstrate that four-fold resolution gains are achievable for the proposed imaging method. We can obtain satisfactory results for both visual and quantitative metrics with one-sixth of the measurements in the conventional imaging correlography. Therefore, the proposed imaging technique is more suitable for long-range practical applications.

Ptychographic imaging of incoherently illuminated extended objects using speckle correlations

A scattering layer is usually considered an obstacle to imaging. However, using speckle correlation imaging techniques, the scattering layer effectively acts as a lens. To date, the speckle correlation imaging method has been limited to imaging sparse samples. Here, we demonstrate imaging of incoherently illuminated extended objects in transmission and around-the-corner geometries. We are able to image extended objects by constraining the illumination spot on the object and then scanning the object. A ptychography algorithm is used to reconstruct the extended object. This work demonstrates the applicability of ptychography to spatially incoherent light and enables a new method of imaging in spectral regions where there is limited choice in optics, such as the terahertz, extreme ultraviolet, and x-ray regions.