Learning to Dehaze from Realistic Scene with A Fast Physics-based Dehazing Network

Dehazing is a popular computer vision topic for long. A real-time dehazing method with reliable performance is highly desired for many applications such as autonomous driving. While recent learning-based methods require datasets containing pairs of hazy images and clean ground truth references, it is generally impossible to capture accurate ground truth in real scenes. Many existing works compromise this difficulty to generate hazy images by rendering the haze from depth on common RGBD datasets using the haze imaging model. However, there is still a gap between the synthetic datasets and real hazy images as large datasets with high-quality depth are mostly indoor and depth maps for outdoor are imprecise. In this paper, we complement the existing datasets with a new, large, and diverse dehazing dataset containing real outdoor scenes from High-Definition (HD) 3D movies. We select a large number of high-quality frames of real outdoor scenes and render haze on them using depth from stereo. Our dataset is more realistic than existing ones and we demonstrate that using this dataset greatly improves the dehazing performance on real scenes. In addition to the dataset, we also propose a light and reliable dehazing network inspired by the physics model. Our approach outperforms other methods by a large margin and becomes the new state-of-the-art method. Moreover, the light-weight design of the network enables our method to run at a real-time speed, which is much faster than other baseline methods

Direct Observation of Photoinduced Charge Separation in Ruthenium Complex/Ni(OH)\u3csub\u3e2\u3c/sub\u3e Nanoparticle Hybrid

Ni(OH)2 have emerged as important functional materials for solar fuel conversion because of their potential as cost-effective bifunctional catalysts for both hydrogen and oxygen evolution reactions. However, their roles as photocatalysts in the photoinduced charge separation (CS) reactions remain unexplored. In this paper, we investigate the CS dynamics of a newly designed hybrid catalyst by integrating a Ru complex with Ni(OH)2 nanoparticles (NPs). Using time resolved X-ray absorption spectroscopy (XTA), we directly observed the formation of the reduced Ni metal site (~60 ps), unambiguously demonstrating CS process in the hybrid through ultrafast electron transfer from Ru complex to Ni(OH)2 NPs. Compared to the ultrafast CS process, the charge recombination in the hybrid is ultraslow (≫50 ns). These results not only suggest the possibility of developing Ni(OH)2 as solar fuel catalysts, but also represent the first time direct observation of efficient CS in a hybrid catalyst using XTA

Enhancement of stimulated Brillouin scattering of higher-order acoustic modes in single-mode optical fiber

This paper was published in Optics Letters and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website: http://www.opticsinfobase.org/abstract.cfm?URI=ol-30-20-2685. Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law.Solving the elastic wave equation exactly for a GeO2-doped silica fiber with a steplike distribution of the longitudinal and shear velocities and density, we have obtained the dispersion, attenuation, and fields of the leaky acoustic modes supported by the fiber. We have developed a model for stimulated Brillouin scattering of these modes in a pump-probe configuration and provided their Brillouin gains and frequencies for an extended range of core sizes and GeO2 doping. Parameter ranges close to cutoff of the acoustic modes and pump depletion enhance the ratio of higher-order peaks to the main peak in the Brillouin spectrum and are suitable for simultaneous strain-temperature sensing.Shahraam Afshar V., V. P. Kalosha, Xiaoyi Bao, Liang Che

GO-FEAP: Global Optimal UAV Planner Using Frontier-Omission-Aware Exploration and Altitude-Stratified Planning

Autonomous exploration is a fundamental problem for various applications of unmanned aerial vehicles(UAVs). Existing methods, however, are demonstrated to static local optima and two-dimensional exploration. To address these challenges, this paper introduces GO-FEAP (Global Optimal UAV Planner Using Frontier-Omission-Aware Exploration and Altitude-Stratified Planning), aiming to achieve efficient and complete three-dimensional exploration. Frontier-Omission-Aware Exploration module presented in this work takes into account multiple pivotal factors, encompassing frontier distance, nearby frontier count, frontier duration, and frontier categorization, for a comprehensive assessment of frontier importance. Furthermore, to tackle scenarios with substantial vertical variations, we introduce the Altitude-Stratified Planning strategy, which stratifies the three-dimensional space based on altitude, conducting global-local planning for each stratum. The objective of global planning is to identify the most optimal frontier for exploration, followed by viewpoint selection and local path optimization based on frontier type, ultimately generating dynamically feasible three-dimensional spatial exploration trajectories. We present extensive benchmark and real-world tests, in which our method completes the exploration tasks with unprecedented completeness compared to state-of-the-art approaches.Comment: 7 pages,29 figure

Local Geometric Distortions Resilient Watermarking Scheme Based on Symmetry

As an efficient watermark attack method, geometric distortions destroy the synchronization between watermark encoder and decoder. And the local geometric distortion is a famous challenge in the watermark field. Although a lot of geometric distortions resilient watermarking schemes have been proposed, few of them perform well against local geometric distortion like random bending attack (RBA). To address this problem, this paper proposes a novel watermark synchronization process and the corresponding watermarking scheme. In our scheme, the watermark bits are represented by random patterns. The message is encoded to get a watermark unit, and the watermark unit is flipped to generate a symmetrical watermark. Then the symmetrical watermark is embedded into the spatial domain of the host image in an additive way. In watermark extraction, we first get the theoretically mean-square error minimized estimation of the watermark. Then the auto-convolution function is applied to this estimation to detect the symmetry and get a watermark units map. According to this map, the watermark can be accurately synchronized, and then the extraction can be done. Experimental results demonstrate the excellent robustness of the proposed watermarking scheme to local geometric distortions, global geometric distortions, common image processing operations, and some kinds of combined attacks