Temporal shape super-resolution by intra-frame motion encoding using high-fps structured light

One of the solutions of depth imaging of moving scene is to project a static pattern on the object and use just a single image for reconstruction. However, if the motion of the object is too fast with respect to the exposure time of the image sensor, patterns on the captured image are blurred and reconstruction fails. In this paper, we impose multiple projection patterns into each single captured image to realize temporal super resolution of the depth image sequences. With our method, multiple patterns are projected onto the object with higher fps than possible with a camera. In this case, the observed pattern varies depending on the depth and motion of the object, so we can extract temporal information of the scene from each single image. The decoding process is realized using a learning-based approach where no geometric calibration is needed. Experiments confirm the effectiveness of our method where sequential shapes are reconstructed from a single image. Both quantitative evaluations and comparisons with recent techniques were also conducted.Comment: 9 pages, Published at the International Conference on Computer Vision (ICCV 2017

Compressive Holographic Video

Compressed sensing has been discussed separately in spatial and temporal domains. Compressive holography has been introduced as a method that allows 3D tomographic reconstruction at different depths from a single 2D image. Coded exposure is a temporal compressed sensing method for high speed video acquisition. In this work, we combine compressive holography and coded exposure techniques and extend the discussion to 4D reconstruction in space and time from one coded captured image. In our prototype, digital in-line holography was used for imaging macroscopic, fast moving objects. The pixel-wise temporal modulation was implemented by a digital micromirror device. In this paper we demonstrate $10\times$ temporal super resolution with multiple depths recovery from a single image. Two examples are presented for the purpose of recording subtle vibrations and tracking small particles within 5 ms.Comment: 12 pages, 6 figure

Illumination coding meets uncertainty learning: toward reliable AI-augmented phase imaging

We propose a physics-assisted deep learning (DL) framework for large space-bandwidth product (SBP) phase imaging. We design an asymmetric coded illumination scheme to encode high-resolution phase information across a wide field-of-view. We then develop a matching DL algorithm to provide large-SBP phase estimation. We show that this illumination coding scheme is highly scalable in achieving flexible resolution, and robust to experimental variations. We demonstrate this technique on both static and dynamic biological samples, and show that it can reliably achieve 5X resolution enhancement across 4X FOVs using only five multiplexed measurements -- more than 10X data reduction over the state-of-the-art. Typical DL algorithms tend to provide over-confident predictions, whose errors are only discovered in hindsight. We develop an uncertainty learning framework to overcome this limitation and provide predictive assessment to the reliability of the DL prediction. We show that the predicted uncertainty maps can be used as a surrogate to the true error. We validate the robustness of our technique by analyzing the model uncertainty. We quantify the effect of noise, model errors, incomplete training data, and "out-of-distribution" testing data by assessing the data uncertainty. We further demonstrate that the predicted credibility maps allow identifying spatially and temporally rare biological events. Our technique enables scalable AI-augmented large-SBP phase imaging with dependable predictions.Published versio