DeOccNet: Learning to See Through Foreground Occlusions in Light Fields

Background objects occluded in some views of a light field (LF) camera can be seen by other views. Consequently, occluded surfaces are possible to be reconstructed from LF images. In this paper, we handle the LF de-occlusion (LF-DeOcc) problem using a deep encoder-decoder network (namely, DeOccNet). In our method, sub-aperture images (SAIs) are first given to the encoder to incorporate both spatial and angular information. The encoded representations are then used by the decoder to render an occlusionfree center-view SAI. To the best of our knowledge, DeOccNet is the first deep learning-based LF-DeOcc method. To handle the insufficiency of training data, we propose an LF synthesis approach to embed selected occlusion masks into existing LF images. Besides, several synthetic and realworld LFs are developed for performance evaluation. Experimental results show that, after training on the generated data, our DeOccNet can effectively remove foreground occlusions and achieves superior performance as compared to other state-of-the-art methods. Source codes are available at: https://github.com/YingqianWang/DeOccNet.Comment: 10 pages, 8 figure

Real-World Light Field Image Super-Resolution via Degradation Modulation

Recent years have witnessed the great advances of deep neural networks (DNNs) in light field (LF) image super-resolution (SR). However, existing DNN-based LF image SR methods are developed on a single fixed degradation (e.g., bicubic downsampling), and thus cannot be applied to super-resolve real LF images with diverse degradation. In this paper, we propose a simple yet effective method for real-world LF image SR. In our method, a practical LF degradation model is developed to formulate the degradation process of real LF images. Then, a convolutional neural network is designed to incorporate the degradation prior into the SR process. By training on LF images using our formulated degradation, our network can learn to modulate different degradation while incorporating both spatial and angular information in LF images. Extensive experiments on both synthetically degraded and real-world LF images demonstrate the effectiveness of our method. Compared with existing state-of-the-art single and LF image SR methods, our method achieves superior SR performance under a wide range of degradation, and generalizes better to real LF images. Codes and models are available at https://yingqianwang.github.io/LF-DMnet/.Comment: 15 pages, 10 figure

A Humanized Anti-VEGF Rabbit Monoclonal Antibody Inhibits Angiogenesis and Blocks Tumor Growth in Xenograft Models

Rabbit antibodies have been widely used in research and diagnostics due to their high antigen specificity and affinity. Though these properties are also highly desirable for therapeutic applications, rabbit antibodies have remained untapped for human disease therapy. To evaluate the therapeutic potential of rabbit monoclonal antibodies (RabMAbs), we generated a panel of neutralizing RabMAbs against human vascular endothelial growth factor-A (VEGF). These neutralizing RabMAbs are specific to VEGF and do not cross-react to other members of the VEGF protein family. Guided by sequence and lineage analysis of a panel of neutralizing RabMAbs, we humanized the lead candidate by substituting non-critical residues with human residues within both the frameworks and the CDR regions. We showed that the humanized RabMAb retained its parental biological properties and showed potent inhibition of the growth of H460 lung carcinoma and A673 rhabdomyosarcoma xenografts in mice. These studies provide proof of principle for the feasibility of developing humanized RabMAbs as therapeutics