Separating Reflection and Transmission Images in the Wild

The reflections caused by common semi-reflectors, such as glass windows, can impact the performance of computer vision algorithms. State-of-the-art methods can remove reflections on synthetic data and in controlled scenarios. However, they are based on strong assumptions and do not generalize well to real-world images. Contrary to a common misconception, real-world images are challenging even when polarization information is used. We present a deep learning approach to separate the reflected and the transmitted components of the recorded irradiance, which explicitly uses the polarization properties of light. To train it, we introduce an accurate synthetic data generation pipeline, which simulates realistic reflections, including those generated by curved and non-ideal surfaces, non-static scenes, and high-dynamic-range scenes.Comment: accepted at ECCV 201

Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs

Human visual system relies on both binocular stereo cues and monocular focusness cues to gain effective 3D perception. In computer vision, the two problems are traditionally solved in separate tracks. In this paper, we present a unified learning-based technique that simultaneously uses both types of cues for depth inference. Specifically, we use a pair of focal stacks as input to emulate human perception. We first construct a comprehensive focal stack training dataset synthesized by depth-guided light field rendering. We then construct three individual networks: a Focus-Net to extract depth from a single focal stack, a EDoF-Net to obtain the extended depth of field (EDoF) image from the focal stack, and a Stereo-Net to conduct stereo matching. We show how to integrate them into a unified BDfF-Net to obtain high-quality depth maps. Comprehensive experiments show that our approach outperforms the state-of-the-art in both accuracy and speed and effectively emulates human vision systems

I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images

Image dehazing has become an important computational imaging topic in the recent years. However, due to the lack of ground truth images, the comparison of dehazing methods is not straightforward, nor objective. To overcome this issue we introduce a new dataset -named I-HAZE- that contains 35 image pairs of hazy and corresponding haze-free (ground-truth) indoor images. Different from most of the existing dehazing databases, hazy images have been generated using real haze produced by a professional haze machine. For easy color calibration and improved assessment of dehazing algorithms, each scene include a MacBeth color checker. Moreover, since the images are captured in a controlled environment, both haze-free and hazy images are captured under the same illumination conditions. This represents an important advantage of the I-HAZE dataset that allows us to objectively compare the existing image dehazing techniques using traditional image quality metrics such as PSNR and SSIM

Thermopile detector of light ellipticity

Polarimetric imaging is widely used in applications from material analysis to biomedical diagnostics, vision and astronomy. The degree of circular polarization, or light ellipticity, is associated with the S3 Stokes parameter which is defined as the difference in the intensities of the left- and right-circularly polarized components of light. Traditional way of determining this parameter relies on using several external optical elements, such as polarizers and wave plates, along with conventional photodetectors, and performing at least two measurements to distinguish left- and right-circularly polarized light components. Here we theoretically propose and experimentally demonstrate a thermopile photodetector element that provides bipolar voltage output directly proportional to the S3 Stokes parameter of the incident light.ope

An Implantable Vascularized Protein Gel Construct That Supports Human Fetal Hepatoblast Survival and Infection by Hepatitis C Virus in Mice

Widely accessible small animal models suitable for the study of hepatitis C virus (HCV) in vivo are lacking, primarily because rodent hepatocytes cannot be productively infected and because human hepatocytes are not easily engrafted in immunodeficient mice.We report here on a novel approach for human hepatocyte engraftment that involves subcutaneous implantation of primary human fetal hepatoblasts (HFH) within a vascularized rat collagen type I/human fibronectin (rCI/hFN) gel containing Bcl-2-transduced human umbilical vein endothelial cells (Bcl-2-HUVEC) in severe combined immunodeficient X beige (SCID/bg) mice. Maturing hepatic epithelial cells in HFH/Bcl-2-HUVEC co-implants displayed endocytotic activity at the basolateral surface, canalicular microvilli and apical tight junctions between adjacent cells assessed by transmission electron microscopy. Some primary HFH, but not Huh-7.5 hepatoma cells, appeared to differentiate towards a cholangiocyte lineage within the gels, based on histological appearance and cytokeratin 7 (CK7) mRNA and protein expression. Levels of human albumin and hepatic nuclear factor 4alpha (HNF4alpha) mRNA expression in gel implants and plasma human albumin levels in mice engrafted with HFH and Bcl-2-HUVEC were somewhat enhanced by including murine liver-like basement membrane (mLBM) components and/or hepatocyte growth factor (HGF)-HUVEC within the gel matrix. Following ex vivo viral adsorption, both HFH/Bcl-2-HUVEC and Huh-7.5/Bcl-2-HUVEC co-implants sustained HCV Jc1 infection for at least 2 weeks in vivo, based on qRT-PCR and immunoelectron microscopic (IEM) analyses of gel tissue.The system described here thus provides the basis for a simple and robust small animal model of HFH engraftment that is applicable to the study of HCV infections in vivo