Blind Image Deblurring via Reweighted Graph Total Variation

Blind image deblurring, i.e., deblurring without knowledge of the blur kernel, is a highly ill-posed problem. The problem can be solved in two parts: i) estimate a blur kernel from the blurry image, and ii) given estimated blur kernel, de-convolve blurry input to restore the target image. In this paper, by interpreting an image patch as a signal on a weighted graph, we first argue that a skeleton image---a proxy that retains the strong gradients of the target but smooths out the details---can be used to accurately estimate the blur kernel and has a unique bi-modal edge weight distribution. We then design a reweighted graph total variation (RGTV) prior that can efficiently promote bi-modal edge weight distribution given a blurry patch. However, minimizing a blind image deblurring objective with RGTV results in a non-convex non-differentiable optimization problem. We propose a fast algorithm that solves for the skeleton image and the blur kernel alternately. Finally with the computed blur kernel, recent non-blind image deblurring algorithms can be applied to restore the target image. Experimental results show that our algorithm can robustly estimate the blur kernel with large kernel size, and the reconstructed sharp image is competitive against the state-of-the-art methods.Comment: 5 pages, submitted to IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, Alberta, Canada, April, 201

Fast Graph Sampling Set Selection Using Gershgorin Disc Alignment

Graph sampling set selection, where a subset of nodes are chosen to collect samples to reconstruct a smooth graph signal, is a fundamental problem in graph signal processing (GSP). Previous works employ an unbiased least-squares (LS) signal reconstruction scheme and select samples via expensive extreme eigenvector computation. Instead, we assume a biased graph Laplacian regularization (GLR) based scheme that solves a system of linear equations for reconstruction. We then choose samples to minimize the condition number of the coefficient matrix---specifically, maximize the smallest eigenvalue $\lambda_{\min}$. Circumventing explicit eigenvalue computation, we maximize instead the lower bound of $\lambda_{\min}$, designated by the smallest left-end of all Gershgorin discs of the matrix. To achieve this efficiently, we first convert the optimization to a dual problem, where we minimize the number of samples needed to align all Gershgorin disc left-ends at a chosen lower-bound target $T$. Algebraically, the dual problem amounts to optimizing two disc operations: i) shifting of disc centers due to sampling, and ii) scaling of disc radii due to a similarity transformation of the matrix. We further reinterpret the dual as an intuitive disc coverage problem bearing strong resemblance to the famous NP-hard set cover (SC) problem. The reinterpretation enables us to derive a fast approximation scheme from a known SC error-bounded approximation algorithm. We find an appropriate target $T$ efficiently via binary search. Extensive simulation experiments show that our disc-based sampling algorithm runs substantially faster than existing sampling schemes and outperforms other eigen-decomposition-free sampling schemes in reconstruction error.Comment: Very fast deterministic graph sampling set selection algorithm without explicit eigen-decompositio

A novel class of microRNA-recognition elements that function only within open reading frames.

MicroRNAs (miRNAs) are well known to target 3' untranslated regions (3' UTRs) in mRNAs, thereby silencing gene expression at the post-transcriptional level. Multiple reports have also indicated the ability of miRNAs to target protein-coding sequences (CDS); however, miRNAs have been generally believed to function through similar mechanisms regardless of the locations of their sites of action. Here, we report a class of miRNA-recognition elements (MREs) that function exclusively in CDS regions. Through functional and mechanistic characterization of these 'unusual' MREs, we demonstrate that CDS-targeted miRNAs require extensive base-pairing at the 3' side rather than the 5' seed; cause gene silencing in an Argonaute-dependent but GW182-independent manner; and repress translation by inducing transient ribosome stalling instead of mRNA destabilization. These findings reveal distinct mechanisms and functional consequences of miRNAs that target CDS versus the 3' UTR and suggest that CDS-targeted miRNAs may use a translational quality-control-related mechanism to regulate translation in mammalian cells

The Pediatric Cell Atlas:Defining the Growth Phase of Human Development at Single-Cell Resolution

Single-cell gene expression analyses of mammalian tissues have uncovered profound stage-specific molecular regulatory phenomena that have changed the understanding of unique cell types and signaling pathways critical for lineage determination, morphogenesis, and growth. We discuss here the case for a Pediatric Cell Atlas as part of the Human Cell Atlas consortium to provide single-cell profiles and spatial characterization of gene expression across human tissues and organs. Such data will complement adult and developmentally focused HCA projects to provide a rich cytogenomic framework for understanding not only pediatric health and disease but also environmental and genetic impacts across the human lifespan

Functional organization of the fusiform gyrus revealed with connectivity profiles

Within the object recognition-related ventral visual stream, the human fusiform gyrus (FG), which topographically connects the striate cortex to the inferior temporal lobe, plays a pivotal role in high-level visual/cognitive functions. However, though there are many previous investigations of distinct functional modules within the FG, the functional organization of the whole FG in its full functional heterogeneity has not yet been established. In the current study, a replicable functional organization of the FG based on distinct anatomical connectivity patterns was identified. The FG was parcellated into medial (FGm), lateral (FGl), and anterior (FGa) regions using diffusion tensor imaging. We validated the reasonability of such an organizational scheme from the perspective of resting-state whole brain functional connectivity patterns and the involvement of functional subnetworks. We found corroborating support for these three distinct modules, and suggest that the FGm serves as a transition region that combines multiple stimuli, the FGl is responsible for categorical recognition, and the FGa is involved in semantic understanding. These findings support two organizational functional transitions of the ventral temporal gyrus, a posterior/anterior direction of visual/semantic processing, and a media/lateral direction of high-level visual processing. Our results may facilitate a more detailed study of the human FG in the future

An Adaptive Perceptual Quantization Algorithm Based on Block-Level JND for Video Coding

It has been widely demonstrated that integrating efficient perceptual measures into traditional video coding framework can improve subjective coding performance significantly. In this paper, we propose a novel block-level JND (just-noticeable-distortion) model, which has not only adjusted pixel-level JND thresholds with more block characteristics, but also integrated them into a block-level model. And the model has been applied for adaptive perceptual quantization for video coding. Experimental results show that our model can save bit rates up to 24.5% on average with negligible degradation of the perceptual quality. ? Springer International Publishing Switzerland 2014.EI054-63887