Traditional fringe-projection three-dimensional (3D) imaging techniques struggle to estimate the shape of high dynamic range (HDR) objects where detected fringes are of limited visibility. Moreover, saturated regions of specular reflections can completely block any fringe patterns, leading to lost depth information. We propose a multi-polarization fringe projection (MPFP) imaging technique that eliminates saturated points and enhances the fringe contrast by selecting the proper polarized channel measurements. The developed technique can be easily extended to include measurements captured under different exposure times to obtain more accurate shape rendering for very HDR objects.

Oral cancer is a growing health issue in a number of low- and middle-income countries (LMIC), particularly in South and Southeast Asia. The described dual-modality, dual-view, point-of-care oral cancer screening device, developed for high-risk populations in remote regions with limited infrastructure, implements autofluorescence imaging (AFI) and white light imaging (WLI) on a smartphone platform, enabling early detection of pre-cancerous and cancerous lesions in the oral cavity with the potential to reduce morbidity, mortality, and overall healthcare costs. Using a custom Android application, this device synchronizes external light-emitting diode (LED) illumination and image capture for AFI and WLI. Data is uploaded to a cloud server for diagnosis by a remote specialist through a web app, with the ability to transmit triage instructions back to the device and patient. Finally, with the on-site specialist’s diagnosis as the gold-standard, the remote specialist and a convolutional neural network (CNN) were able to classify 170 image pairs into ‘suspicious’ and ‘not suspicious’ with sensitivities, specificities, positive predictive values, and negative predictive values ranging from 81.25% to 94.94%.

With the goal to screen high-risk populations for oral cancer in low- and middle-income countries (LMICs), we have developed a low-cost, portable, easy to use smartphone-based intraoral dual-modality imaging platform. In this paper we present an image classification approach based on autofluorescence and white light images using deep learning methods. The information from the autofluorescence and white light image pair is extracted, calculated, and fused to feed the deep learning neural networks. We have investigated and compared the performance of different convolutional neural networks, transfer learning, and several regularization techniques for oral cancer classification. Our experimental results demonstrate the effectiveness of deep learning methods in classifying dual-modal images for oral cancer detection.

The interferometry technique is commonly used to obtain the phase information of an object in optical metrology. The obtained wrapped phase is subject to a 2π ambiguity. To remove the ambiguity and obtain the correct phase, phase unwrapping is essential. Conventional phase unwrapping approaches are time-consuming and noise sensitive. To address those issues, we propose a new approach, where we transfer the task of phase unwrapping into a multi-class classification problem and introduce an efficient segmentation network to identify classes. Moreover, a noise-to-noise denoised network is integrated to preprocess noisy wrapped phase. We have demonstrated the proposed method with simulated data and in a real interferometric system.

We propose a polarization demosaicing convolutional neural network to address the image demosaicing issue, the last unsolved issue in microgrid polarimeters. This network learns an end-to-end mapping between the mosaic images and full-resolution ones. Skip connections and customized loss function are used to boost the performance. Experimental results show that our proposed network outperforms other state-of-the-art methods by a large margin in terms of quantitative measures and visual quality.

We present a modified optimal transport (OT) ray-mapping approach for designing freeform illumination optics. After mapping the source intensity into a virtual irradiance distribution under stereographic projection, we employ an advanced OT map computation method with the ability to tackle nonstandard boundary conditions. Following the computed map, we construct the freeform optical surface directly from normal vectors by requiring that the chord between two adjacent points is perpendicular to the average of the two normal vectors at these two points and enforcing this relationship with a least squares method. Examples of designing freeform lenses for LED sources show that we can produce various uniform illumination patterns with high optical efficiencies.