Visibility Restoration for Single Hazy Image Using Dual Prior Knowledge

Single image haze removal has been a challenging task due to its super ill-posed nature. In this paper, we propose a novel single image algorithm that improves the detail and color of such degraded images. More concretely, we redefine a more reliable atmospheric scattering model (ASM) based on our previous work and the atmospheric point spread function (APSF). Further, by taking the haze density spatial feature into consideration, we design a scene-wise APSF kernel prediction mechanism to eliminate the multiple-scattering effect. With the redefined ASM and designed APSF, combined with the existing prior knowledge, the complex dehazing problem can be subtly converted into one-dimensional searching problem, which allows us to directly obtain the scene transmission and thereby recover visually realistic results via the proposed ASM. Experimental results verify that our algorithm outperforms several state-of-the-art dehazing techniques in terms of robustness, effectiveness, and efficiency

A Low Complexity Active Sensing and Inspection System for Monitoring of Moveable Radiation Environments

Due to the portable property of moveable radiation sources, the traditional monitoring method is becoming increasingly unsuitable and it is urgent to provide an effective and low-cost method. This paper presents an active monitoring scheme for moveable radiation environments, the in situ monitoring, including a radiation detection node, an infrared proximity node, and an alarm node; these three schemes communicate with each other through the ZigBee wireless network. An active monitoring mechanism which realizes the automatic judgment of radiation source inbound or outbound state is proposed, thereby automatically switching the data sampling mode under different working conditions, so as to reduce the energy consumption of nodes. Based on the mobile terminal client application to interact with the monitoring center, a collaborative management mode between enterprise users and the environmental protection department is realized. A testbed of a simple active sensing and inspection system is created to test its user interaction capabilities. Experimental results prove that the system schedule proposed can effectively detect and dynamically monitor the moveable radiation source. The system can be easily replicated and extended to more environmental monitoring network

Multi-objective robust resource allocation for secure communication in full-duplex MIMO systems

Abstract In this paper, we study robust resource allocation for the multi-user full-duplex (FD) multiple-input multiple-output (MIMO) communication systems. Particularly, we aim at minimizing uplink (UL) transmit power and downlink (DL) transmit power simultaneously while guaranteeing the quality of service (QoS) requirements regarding secure UL and DL communication, under the consideration of the imperfect channel state information (CSI) of the wiretap channels and the inter-user interference channels. In view of the conflicting of two objectives, we propose a multi-objective optimization (MOO) framework to achieve the trade-off between them. The formulated MOO problem is non-convex and intractable. By employing the weighted Tchebycheff, the Taylor series expansion, and the S-procedure approaches, we convert the MOO problem into the convex one and propose an iterative algorithm to solve it optimally. Simulation results not only demonstrate an interesting trade-off between the considered conflicting objectives but also show the efficiency of our proposed robust resource allocation designs

LEC-AODV Routing Protocol Based on Load and Energy Classification

Abstract： ： ： ：Now widely used AODV routing protocol does not take into account the node's load size and energy level. During the route discovery process, AODV routing protocol often has the blindness and makes it easy to bring about network congestion and consume some node's energy excessively. To solve this problem, this paper presents a new LEC-AODV routing protocol based on load and energy classification, which makes a different response according to the node's load size and energy level. Simulation results show that the protocol does not significantly increase the complexity of traditional AODV routing algorithm when packet delivery ratio, average end-to-end delay and network lifetime and other indicators have improved in varying degrees. At the same time it can bypass the nodes with heavy load and low energy, thereby achieve a certain degree of flow and energy balance and successfully solve the QoS problem in tactical MANETs

A Low-Light Image Enhancement Method Based on Image Degradation Model and Pure Pixel Ratio Prior

Images captured in low-light conditions are prone to suffer from low visibility, which may further degrade the performance of most computational photography and computer vision applications. In this paper, we propose a low-light image degradation model derived from the atmospheric scattering model, which is simple but effective and robust. Then, we present a physically valid image prior named pure pixel ratio prior, which is a statistical regularity of extensive nature clear images. Based on the proposed model and the image prior, a corresponding low-light image enhancement method is also presented. In this method, we first segment the input image into scenes according to the brightness similarity and utilize a high-efficiency scene-based transmission estimation strategy rather than the traditional per-pixel fashion. Next, we refine the rough transmission map, by using a total variation smooth operator, and obtain the enhanced image accordingly. Experiments on a number of challenging nature low-light images verify the effectiveness and robustness of the proposed model, and the corresponding method can show its superiority over several state of the arts

A Single Image Dehazing Method Using Average Saturation Prior

Outdoor images captured in bad weather are prone to yield poor visibility, which is a fatal problem for most computer vision applications. The majority of existing dehazing methods rely on an atmospheric scattering model and therefore share a common limitation; that is, the model is only valid when the atmosphere is homogeneous. In this paper, we propose an improved atmospheric scattering model to overcome this inherent limitation. By adopting the proposed model, a corresponding dehazing method is also presented. In this method, we first create a haze density distribution map of a hazy image, which enables us to segment the hazy image into scenes according to the haze density similarity. Then, in order to improve the atmospheric light estimation accuracy, we define an effective weight assignment function to locate a candidate scene based on the scene segmentation results and therefore avoid most potential errors. Next, we propose a simple but powerful prior named the average saturation prior (ASP), which is a statistic of extensive high-definition outdoor images. Using this prior combined with the improved atmospheric scattering model, we can directly estimate the scene atmospheric scattering coefficient and restore the scene albedo. The experimental results verify that our model is physically valid, and the proposed method outperforms several state-of-the-art single image dehazing methods in terms of both robustness and effectiveness

Distributed Compressive Video Sensing with Mixed Multihypothesis Prediction

Traditional video acquisition systems require complex data compression at the encoder, which makes them unacceptable for resource-limited applications such as wireless multimedia sensor networks (WMSNs). To address this problem, distributed compressive video sensing (DCVS) represents a novel sensing approach with a simple encoder. This method shifts the computational burden from the encoder to the decoder and needs a robust reconstruction algorithm. In this paper, a mixed measurement-based multihypothesis (MH) reconstruction algorithm (mixed-MH) is proposed for DCVS to improve the reconstruction quality at low sampling rates. Considering the inaccuracy of MH prediction when measurements are insufficient, the available side information (SI) is resampled to obtain the artificial measurements, which are then integrated into real measurements via regularization. Furthermore, to avoid the negative effect of SI at high sampling rates, an adaptive regularization parameter is designed to balance the contributions of real and artificial measurements at different sampling rates. The experimental results demonstrate that the proposed mixed-MH prediction scheme outperforms other state-of-the-art algorithms in the reconstruction quality at the same low sampling rate