Development of high-resolution optical tomography with a larger-size projection acquisition

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on November 11, 2008)Includes bibliographical references.Thesis (M.S.) University of Missouri-Columbia 2007.Dissertations, Academic -- University of Missouri--Columbia -- Biological engineering.In the industrial countries, the mortality from arteriosclerosis and cardiovascular diseases has been decreased, but they remain the most common circulatory disease. Recently, new tissue engineering technologies such as tissue-engineered blood vessels (TEBV), produced from a patient's own cells, are becoming available to treat these diseases. In order to monitor the quality of TEBV and assess biomechanical properties, a nondestructive and minimally invasive method is necessary. Optical Tomography (OT) is a proper imaging method because it satisfies the above requirements. Besides, the benefits of OT include: low cost, simple design and ultra fast acquisition. The purpose of this project was to develop an ultra-fast 3D OT scanner by using a beam expander and larger-size CCD cameras instead of pencil beam and smaller-size detectors. The new device is capable of imaging an axial section of about 4mm height in one single revolution. The new OT scanner will primarily be used to obtain the biomechanical properties, the geometry and defects of TEBV. The new device was evaluated by using phantom studies and then tested its performance for the thin tissue layer of TEBV, obtained from Cytograft Tissue Engineering. The results show that the new system has the potential to be a low cost, high tissue contrast, rapid and simple scanner

Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution

Convolutional neural networks have recently demonstrated high-quality reconstruction for single-image super-resolution. In this paper, we propose the Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively reconstruct the sub-band residuals of high-resolution images. At each pyramid level, our model takes coarse-resolution feature maps as input, predicts the high-frequency residuals, and uses transposed convolutions for upsampling to the finer level. Our method does not require the bicubic interpolation as the pre-processing step and thus dramatically reduces the computational complexity. We train the proposed LapSRN with deep supervision using a robust Charbonnier loss function and achieve high-quality reconstruction. Furthermore, our network generates multi-scale predictions in one feed-forward pass through the progressive reconstruction, thereby facilitates resource-aware applications. Extensive quantitative and qualitative evaluations on benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of speed and accuracy.Comment: This work is accepted in CVPR 2017. The code and datasets are available on http://vllab.ucmerced.edu/wlai24/LapSRN

Unsupervised Representation Learning by Sorting Sequences

We present an unsupervised representation learning approach using videos without semantic labels. We leverage the temporal coherence as a supervisory signal by formulating representation learning as a sequence sorting task. We take temporally shuffled frames (i.e., in non-chronological order) as inputs and train a convolutional neural network to sort the shuffled sequences. Similar to comparison-based sorting algorithms, we propose to extract features from all frame pairs and aggregate them to predict the correct order. As sorting shuffled image sequence requires an understanding of the statistical temporal structure of images, training with such a proxy task allows us to learn rich and generalizable visual representation. We validate the effectiveness of the learned representation using our method as pre-training on high-level recognition problems. The experimental results show that our method compares favorably against state-of-the-art methods on action recognition, image classification and object detection tasks.Comment: ICCV 2017. Project page: http://vllab1.ucmerced.edu/~hylee/OPN