Convolutional Deblurring for Natural Imaging

In this paper, we propose a novel design of image deblurring in the form of one-shot convolution filtering that can directly convolve with naturally blurred images for restoration. The problem of optical blurring is a common disadvantage to many imaging applications that suffer from optical imperfections. Despite numerous deconvolution methods that blindly estimate blurring in either inclusive or exclusive forms, they are practically challenging due to high computational cost and low image reconstruction quality. Both conditions of high accuracy and high speed are prerequisites for high-throughput imaging platforms in digital archiving. In such platforms, deblurring is required after image acquisition before being stored, previewed, or processed for high-level interpretation. Therefore, on-the-fly correction of such images is important to avoid possible time delays, mitigate computational expenses, and increase image perception quality. We bridge this gap by synthesizing a deconvolution kernel as a linear combination of Finite Impulse Response (FIR) even-derivative filters that can be directly convolved with blurry input images to boost the frequency fall-off of the Point Spread Function (PSF) associated with the optical blur. We employ a Gaussian low-pass filter to decouple the image denoising problem for image edge deblurring. Furthermore, we propose a blind approach to estimate the PSF statistics for two Gaussian and Laplacian models that are common in many imaging pipelines. Thorough experiments are designed to test and validate the efficiency of the proposed method using 2054 naturally blurred images across six imaging applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin

Hard Real-Time Networking on Firewire

This paper investigates the possibility of using standard, low-cost, widely used FireWire as a new generation fieldbus medium for real-time distributed control applications. A real-time software subsystem, RT-FireWire was designed that can, in combination with Linux-based real-time operating system, provide hard real-time communication over FireWire. In addition, a high-level module that can emulate Ethernet over RT-FireWire was implemented. This additional module enables existing IP-based real-time communication frameworks to work on top of FireWire. The real-time behavior of RT-FireWire was demonstrated with a simple control setup. Furthermore, an outlook of the future development on RT-FireWire is given

How is Gaze Influenced by Image Transformations? Dataset and Model

Data size is the bottleneck for developing deep saliency models, because collecting eye-movement data is very time consuming and expensive. Most of current studies on human attention and saliency modeling have used high quality stereotype stimuli. In real world, however, captured images undergo various types of transformations. Can we use these transformations to augment existing saliency datasets? Here, we first create a novel saliency dataset including fixations of 10 observers over 1900 images degraded by 19 types of transformations. Second, by analyzing eye movements, we find that observers look at different locations over transformed versus original images. Third, we utilize the new data over transformed images, called data augmentation transformation (DAT), to train deep saliency models. We find that label preserving DATs with negligible impact on human gaze boost saliency prediction, whereas some other DATs that severely impact human gaze degrade the performance. These label preserving valid augmentation transformations provide a solution to enlarge existing saliency datasets. Finally, we introduce a novel saliency model based on generative adversarial network (dubbed GazeGAN). A modified UNet is proposed as the generator of the GazeGAN, which combines classic skip connections with a novel center-surround connection (CSC), in order to leverage multi level features. We also propose a histogram loss based on Alternative Chi Square Distance (ACS HistLoss) to refine the saliency map in terms of luminance distribution. Extensive experiments and comparisons over 3 datasets indicate that GazeGAN achieves the best performance in terms of popular saliency evaluation metrics, and is more robust to various perturbations. Our code and data are available at: https://github.com/CZHQuality/Sal-CFS-GAN

High Speed Dim Air Target Detection Using Airborne Radar under Clutter and Jamming Effects

The challenging potential problems associated with using airborne radar in detection of high Speed Maneuvering Dim Target (HSMDT) are the highly noise, jamming and clutter effects. The problem is not only how to remove clutter and jamming as well as the range migration and Doppler ambiguity estimation problems due to high relative speed between the targets and airborne radar. Some of the recently published works ignored the range migration problems, while the others ignored the Doppler ambiguity estimation. In this paper a new hybrid technique using Optimum Space Time Adaptive Processing (OSTAP), Second Order Keystone Transform (SOKT), and the Improved Fractional Radon Transform (IFrRT) was proposed. The OSTAP was applied as anti-jamming and clutter rejection method, the SOKT corrects the range curvature and part of the range walk, then the IFrRT estimates the target’ radial acceleration and corrects the residual range walk. The simulation demonstrates the validity and effectiveness of the proposed technique, and its advantages over the previous researches by comparing its probability of detection with the traditional methods. The new approach increases the probability of detection, and also overcomes the limitation of Doppler frequency ambiguity

\u3ci\u3eAmEx\u3c/i\u3e and Post-Cartesian Antitrust

I. Introduction II. Situating American Express ... A. Different Sides of the American Express Opinion ... 1. Two-Sided Markets ... 2. The American Express Opinion ... B. The Real Issue Is Messy, Not Two-Sided, Markets … C. The Many Messes of Modern Markets III. Competition in Messy Markets ... A. Simple Competition in Simple Markets ... B. More Complex Competition in Messier Markets ... C. American Express: The Competition Is in the Pudding IV. The Many Sides of AmEx’s Rightness ... A. A Burden Best Born by Plaintiffs ... B. Economic Theory as a Question of Law or of Fact? V. Conclusio

Semi-autonomous scheme for pushing micro-objects

-In many microassembly applications, it is often desirable to position and orient polygonal micro-objects lying on a planar surface. Pushing micro-objects using point contact provides more flexibility and less complexity compared to pick and place operation. Due to the fact that in micro-world surface forces are much more dominant than inertial forces and these forces are distributed unevenly, pushing through the center of mass of the micro-object will not yield a pure translational motion. In order to translate a micro-object, the line of pushing should pass through the center of friction. In this paper, a semi-autonomous scheme based on hybrid vision/force feedback is proposed to push microobjects with human assistance using a custom built telemicromanipulation setup to achieve pure translational motion. The pushing operation is divided into two concurrent processes: In one process human operator who acts as an impedance controller alters the velocity of the pusher while in contact with the micro-object through scaled bilateral teleoperation with force feedback. In the other process, the desired line of pushing for the micro-object is determined continuously using visual feedback procedures so that it always passes through the varying center of friction. Experimental results are demonstrated to prove nanoNewton range force sensing, scaled bilateral teleoperation with force feedback and pushing microobjects

Market efficiency today

This CFS Working Paper has been presented at the CFSsymposium "Market Efficiency Today" held in Frankfurt/Main on October 6, 2005. In 2004 the Center for Financial Studies (CFS) in cooperation with the Johann Wolfgang Goethe University, Frankfurt/Main established an international academic prize, which is to be known as The Deutsche Bank Prize in Financial Economics. The prize will honor an internationally renowned researcher who has excelled through influential contributions to research in the fields of finance and money and macroeconomics, and whose work has lead to practice and policy-relevant results. The Deutsche Bank Prize in Financial Economics has been awarded for the first time in October 2005. The prize, sponsored by the Stiftungsfonds Deutsche Bank im Stifterverband für die Deutsche Wissenschaft, carries a cash award of € 50,000. The prize will be awarded every two years and the prize holder will be appointed a "Distinguished Fellow" of the CFS. The role of media partner for the Deutsche Bank Prize in Financial Economics is to be filled by the internationally renowned publication, The Economist and the Handelsblatt, the leading German-language financial and business newspaper