水下图像处理技术研究综述

随着自主式水下机器人的发展，水下探测技术成为新的研究热点。然而，吸收效应和散射效应导致水下获取的图像存在雾化和色彩偏差等缺陷。降质的水下图像在一定程度上降低了水下目标识别的准确性。为了改善水下图像质量，国内外学者对水下图像处理方法进行了深入研究。因水下图像处理方法对提升水下目标识别准确性具有良好的促进作用，故其具有重要的研究与分析价值。介绍了水下成像模型，分析了水下图像视觉质量下降的原理；根据水下物理成像模型将水下图像处理方法分为水下图像增强与水下图像复原，并分别对两类方法的研究现状进行分析与归纳；最后，总结与讨论了各类方法的优缺点，并展望了未来的发展方向。     

A survey on analysis and implementation of state-of-the-art haze removal techniques

Haze is a poor-quality state described by the opalescent appearance of the atmosphere which reduces the visibility. It is caused by high concentrations of atmospheric air pollutants, such as dust, smoke and other particles that scatter and absorb sunlight. The poor visibility can result in the failure of multiple computer vision applications such as smart transport systems, image processing, object detection, surveillance etc. One of the major issues in the field of image processing is the restoration of images that are corrupted due to different degradations. Typically, the images or videos captured in the outside environment have low contrast, colour fade and restricted visibility due to suspended particles of the atmosphere that directly influence the image quality. This can cause difficulty in identifying the objects in the captured hazy images or frames. To address this problem, several image dehazing techniques have been developed in the literature, each of which has its own advantages and limitations, but effective image restoration remains a challenging task. In recent times, various learning (Machine learning & Deep learning) based methods greatly condensed the drawbacks of manual design of haze related features and reduces the difficulty in efficient restoration of images with less computational time and cost. The current state-of-the-art methods for haze free images, mainly from the last decade, are thoroughly examined in this survey. Moreover, this paper systematically summarizes the hardware implementations of various haze removal methods in real time. It is with the hope that this current survey acts as a reference for researchers in this scientific area and to provide a direction for future improvements based on current achievements.     

Die Baugesellschaft Gebrüder Rank und ihre spanischen Ziegelgewölbe in München

The Rank construction company and the introduction of brick vaults in Munich after 1945 The construction of vaults without scaffolding with thin bricks has a centuries‐old tradition, particularly in Spain and Italy. Since the beginning of the 19^th century, it has been successfully spread mainly on the American double continent. The Rank Brothers construction company in Munich is known to date as one of those construction companies that began building with concrete in its home city very early on. New investigations, which were made possible above all by the provision of archive material by Paul Basiner, now point to an additional focus of this company's work. The architects and engineers of the company had been also able to familiarise themselves with this traditional Spanish brick‐vaulted construction method via the branch office in Spain, which was established from 1911 on. This knowledge was particularly in demand in the years following World War II, when the destroyed vaults had to be rebuilt with simple means and low material consumption. Together with Carl Sattler, who had become acquainted with similar construction methods in Italy, brick vaults were initially used to complete the construction of the Landeszentralbank in Munich. In the following years, Rank was able to provide many buildings in and around Munich with brick vaults. It was not until the 1960s that this construction technique went out of fashion and was thus somewhat forgotten.     

Restoration and functionality assessment of a community subjected to tornado hazard

The loss of infrastructure functionality for a community from a tornado can significantly affect economic vitality and result in substantial social disruption. The duration of the loss of functionality is a decisive factor in the magnitude of the economic disruption caused by a tornado. Therefore, quantitative models for the post-tornado restoration processes are needed in order to provide risk-informed decision support. In this study, the water network, electric power network, school buildings, residential buildings and businesses were modelled with their relative spatial distribution along with select dependencies and cross-dependencies in order to investigate the restoration of a community in the aftermath of a disaster. A tornado path was simulated using statistics from a historical tornado database. Based on the damage level of the components and their connectivity throughout the community, the functionality of the supplier nodes was assessed through a Monte Carlo simulation. Finally, the repair time of the damaged components was modelled to capture how the demands may be satisfied over time until eventually full restoration is achieved. Then, two metrics were defined to highlight business and social disruption following a tornado. The approach presented herein can be implemented in future studies to quantify indirect economic loss and social disruption resulting from a tornado.     

矿山地质环境恢复治理技术方法研究

自工业革命以来人类社会的发展和经济的建设就离不开对矿产资源的使用。我国自改革开放以来,经济得到了前所未有的飞速发展,人民的生活水平日益提高,但经济高速发展的背后,存在着对矿产资源过度开采和使用的问题。矿产资源的不合理开采和使用对原有的自然环境造成了严重的破坏,为了改善此现状,对矿山地质环境的恢复治理技术的研究就至关重要。本文就矿山地质恢复治理的意义进行阐述,进一步对其方法进行探究,最终提出有关的措施和方法。     

企业开展矿山修复业务的几点思考

目前,中国矿山生态修复中存在诸多历史遗留问题。国家着手推动矿山治理和生态修复,已经取得了一些成效,但仍存在资金需求缺口大,修复后矿山土地使用确权、土地流转、开发利用等方面的问题。从国家政策支持、治理资金出资模式等方面对矿山修复进行了思考。     

Optimization and regularization of complex task decomposition for blind removal of multi-factor degradation

Most existing image restoration methods based on deep neural networks are developed for images which only degraded by a single degradation mode and imaging under an ideal condition. They cannot be directly used to restore the images degraded by multi-factor coupling. A complex task decomposition regularization optimization strategy (TDROS) is proposed to solve the problem. The restoration of images degraded by multi-factor coupling is a complex task that can be solved by separating these multiple factors, that is, breaking the complex task into numbers of simpler tasks to make the entire complex problem be overcome more easily. Motivated by this idea, the TDROS decomposes the complex task of image restoration into two sub-task: the potential task constrained by regularization and the main task for reconstructing high-definition images. In TDROS, the front of the neural network is focused on the restoration of images degraded by additive noise, while the other part of the network is focused mainly on the restoration of images degraded by blur. We applied the TDROS to an 11-layer convolutional neural network (CNN) and compared it with initial CNNs from the aspects of restoration accuracy and generalization ability. Based on these results, we used TDROS to design a novel network model for the restoration of atmospheric turbulence-degraded images. The experimental results demonstrate that the proposed TDROS can improve the generalization ability of the existing network more effectively than current popular methods, offering a better solution for the problem of severely degraded image restoration. Moreover, the TDROS concept provides a flexible framework for low-level visual complex tasks and can be easily incorporated into existing CNNs.     

Topology management techniques for tolerating node failures in wireless sensor networks: A survey

In wireless sensor networks (WSNs) nodes often operate unattended in a collaborative manner to perform some tasks. In many applications, the network is deployed in harsh environments such as battlefield where the nodes are susceptible to damage. In addition, nodes may fail due to energy depletion and breakdown in the onboard electronics. The failure of nodes may leave some areas uncovered and degrade the fidelity of the collected data. However, the most serious consequence is when the network gets partitioned into disjoint segments. Losing network connectivity has a very negative effect on the applications since it prevents data exchange and hinders coordination among some nodes. Therefore, restoring the overall network connectivity is very crucial. Given the resource-constrained setup, the recovery should impose the least overhead and performance impact. This paper focuses on network topology management techniques for tolerating/handling node failures in WSNs. Two broad categories based on reactive and proactive methods have been identified for classifying the existing techniques. Considering these categories, a thorough analysis and comparison of all the recent works have been provided. Finally, the paper is concluded by outlining open issues that warrant additional research.     

矿业废弃地的生态恢复与重建研究

大量的矿产资源开采,对生态破坏、环境污染十分严重,并形成了很多矿业废弃地.本文在国内外矿业废弃地生态恢复现状的基础上,对生态恢复与重建的方法进行了综述,并提出其存在的问题.