Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal

In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image. We propose a deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional neural network (CNN). We further extend the candidate set of motion kernels predicted by the CNN using carefully designed image rotations. A Markov random field model is then used to infer a dense non-uniform motion blur field enforcing motion smoothness. Finally, motion blur is removed by a non-uniform deblurring model using patch-level image prior. Experimental evaluations show that our approach can effectively estimate and remove complex non-uniform motion blur that is not handled well by previous approaches.Comment: This is a final version accepted by CVPR 201

María José Domínguez Vázquez, Mónica Mirazo Balsa and Carlos Valcárcel Riveiro (Eds.). Studies on Multilingual Lexicography.

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Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements

Background subtraction has been a fundamental and widely studied task in video analysis, with a wide range of applications in video surveillance, teleconferencing and 3D modeling. Recently, motivated by compressive imaging, background subtraction from compressive measurements (BSCM) is becoming an active research task in video surveillance. In this paper, we propose a novel tensor-based robust PCA (TenRPCA) approach for BSCM by decomposing video frames into backgrounds with spatial-temporal correlations and foregrounds with spatio-temporal continuity in a tensor framework. In this approach, we use 3D total variation (TV) to enhance the spatio-temporal continuity of foregrounds, and Tucker decomposition to model the spatio-temporal correlations of video background. Based on this idea, we design a basic tensor RPCA model over the video frames, dubbed as the holistic TenRPCA model (H-TenRPCA). To characterize the correlations among the groups of similar 3D patches of video background, we further design a patch-group-based tensor RPCA model (PG-TenRPCA) by joint tensor Tucker decompositions of 3D patch groups for modeling the video background. Efficient algorithms using alternating direction method of multipliers (ADMM) are developed to solve the proposed models. Extensive experiments on simulated and real-world videos demonstrate the superiority of the proposed approaches over the existing state-of-the-art approaches.Comment: To appear in IEEE TI

Long and Diverse Text Generation with Planning-based Hierarchical Variational Model

Existing neural methods for data-to-text generation are still struggling to produce long and diverse texts: they are insufficient to model input data dynamically during generation, to capture inter-sentence coherence, or to generate diversified expressions. To address these issues, we propose a Planning-based Hierarchical Variational Model (PHVM). Our model first plans a sequence of groups (each group is a subset of input items to be covered by a sentence) and then realizes each sentence conditioned on the planning result and the previously generated context, thereby decomposing long text generation into dependent sentence generation sub-tasks. To capture expression diversity, we devise a hierarchical latent structure where a global planning latent variable models the diversity of reasonable planning and a sequence of local latent variables controls sentence realization. Experiments show that our model outperforms state-of-the-art baselines in long and diverse text generation.Comment: To appear in EMNLP 201

A Rice Gene of <i>De Novo</i> Origin Negatively Regulates Pathogen-Induced Defense Response

How defense genes originated with the evolution of their specific pathogen-responsive traits remains an important problem. It is generally known that a form of duplication can generate new genes, suggesting that a new gene usually evolves from an ancestral gene. However, we show that a new defense gene in plants may evolve by de novo origination, resulting in sophisticated disease-resistant functions in rice. Analyses of gene evolution showed that this new gene, OsDR10, had homologs only in the closest relative, Leersia genus, but not other subfamilies of the grass family; therefore, it is a rice tribe-specific gene that may have originated de novo in the tribe. We further show that this gene may evolve a highly conservative rice-specific function that contributes to the regulation difference between rice and other plant species in response to pathogen infections. Biologic analyses including gene silencing, pathologic analysis, and mutant characterization by transformation showed that the OsDR10-suppressed plants enhanced resistance to a broad spectrum of Xanthomonas oryzae pv. oryzae strains, which cause bacterial blight disease. This enhanced disease resistance was accompanied by increased accumulation of endogenous salicylic acid (SA) and suppressed accumulation of endogenous jasmonic acid (JA) as well as modified expression of a subset of defense-responsive genes functioning both upstream and downstream of SA and JA. These data and analyses provide fresh insights into the new biologic and evolutionary processes of a de novo gene recruited rapidly.</p