Image Restoration using Total Variation Regularized Deep Image Prior

In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction. Traditional regularizers, such as total variation (TV), rely on analytical models of sparsity. However, increasingly the field is moving towards trainable models, inspired from deep learning. Deep image prior (DIP) is a recent regularization framework that uses a convolutional neural network (CNN) architecture without data-driven training. This paper extends the DIP framework by combining it with the traditional TV regularization. We show that the inclusion of TV leads to considerable performance gains when tested on several traditional restoration tasks such as image denoising and deblurring

SIMBA: scalable inversion in optical tomography using deep denoising priors

Two features desired in a three-dimensional (3D) optical tomographic image reconstruction algorithm are the ability to reduce imaging artifacts and to do fast processing of large data volumes. Traditional iterative inversion algorithms are impractical in this context due to their heavy computational and memory requirements. We propose and experimentally validate a novel scalable iterative mini-batch algorithm (SIMBA) for fast and high-quality optical tomographic imaging. SIMBA enables highquality imaging by combining two complementary information sources: the physics of the imaging system characterized by its forward model and the imaging prior characterized by a denoising deep neural net. SIMBA easily scales to very large 3D tomographic datasets by processing only a small subset of measurements at each iteration. We establish the theoretical fixedpoint convergence of SIMBA under nonexpansive denoisers for convex data-fidelity terms. We validate SIMBA on both simulated and experimentally collected intensity diffraction tomography (IDT) datasets. Our results show that SIMBA can significantly reduce the computational burden of 3D image formation without sacrificing the imaging quality.https://arxiv.org/abs/1911.13241First author draf

Attention, Please! Adversarial Defense via Attention Rectification and Preservation

This study provides a new understanding of the adversarial attack problem by examining the correlation between adversarial attack and visual attention change. In particular, we observed that: (1) images with incomplete attention regions are more vulnerable to adversarial attacks; and (2) successful adversarial attacks lead to deviated and scattered attention map. Accordingly, an attention-based adversarial defense framework is designed to simultaneously rectify the attention map for prediction and preserve the attention area between adversarial and original images. The problem of adding iteratively attacked samples is also discussed in the context of visual attention change. We hope the attention-related data analysis and defense solution in this study will shed some light on the mechanism behind the adversarial attack and also facilitate future adversarial defense/attack model design

The Effect of Transport Amenities on Customer Satisfaction: An Empirical Study from the Online Travel Community

Few studies thus far have examined add-on services associated with mitigating hotel location disadvantages. Drawing on the Elaboration Likelihood Model, we in this study consider the variety of transport amenities as a peripheral cue and propose an econometric model that explores the impact of the transport amenities on customer satisfaction. We estimate the model using 187447 reviews assembled from a well-known online travel community in China. The results show that the variety of transport amenities has a significant positive impact on customer satisfaction. Furthermore, we find that the travelers’ type and transport convenience have a moderating effect on this relationship. From the perspective of the three-factor theory, we further reveal that the transport amenity is a basic factor for business travelers but an excitement factor for leisure travelers. A variety of robustness tests show that the conclusion of this study is robust

Helping Beginning Vloggers to Overcome Cold Start: the Perspective of Identity Construction

Beginning vloggers’ low enthusiasm for Vlog creation has garnered little consideration, even though a social media platform can highly improve user stickiness and user activity by engaging users to generate content. This paper investigates the effects of extrinsic and intrinsic motivations on Vlog creative behavior mediated by cognition and emotion based on social cognitive theory and self-discrepancy theory. The analysis of 342 questionnaire surveys shows that intrinsic motivation (social interaction and social cues presentation) positively affects identity construction and positive emotions. In contrast, extrinsic motivation (community incentives and social norms) only positively affects identity construction and does not significantly influence positive emotions. Identity construction and positive emotions further significantly affect the creative behavior of beginning vloggers. The results reveal the process of Vlog creative behavior and have important practical implications for enhancing the platform performance