Fog cover is generally present in outdoor scenes, which limits the potential for efficient information extraction from images. In this paper, the goal of the developed algorithm is to obtain an optimal transmission map as well as to remove hazes from a single input image. To solve the problem, we meticulously analyze the optical model and recast the initial transmission map under an additional boundary prior. For better preservation of the results, the difference-structure-preservation dictionary could be learned, such that the local consistency features of the transmission map could be well preserved after coefficient shrinkage. Experimental results show that the method preserves the natural appearance of the image.
The Fast handoff for Mobile IPv6 (FMIPv6) and the Synchronized Scan are the most popular solutions to reduce the handoff latency in link layer and network layer respectively. However, like most previous work on reducing the handoff, they do not consider the affection of fluctuation in wireless networks. And as we know, most existing works concentrate on either the link layer handoff or the network layer handoff. However, when using FMIPv6 in the real IEEE 802.11, the link layer trigger is hard to control, so it is difficult to make sure that the MN goes into the predictive mode with the correct prediction with the predefined parameters. In this paper, we proposed the real-time adaptive link layer triggering (RALT) scheme utilizing FFT. It is unveiled that, the FFT-based signal strength decay detection is a feasible method to detect the signal variations of the current AP. Considering both the link layer handoff latency and the network layer handoff latency, the extended FMIPv6 and SynScan integration mechanism based on RALT (FSIR) is proposed to achieve fast handoff in the real IEEE 802.11 WLANs. And we validated the FSIR proposed here through simulation, simulation results show that fast handoff can be achieved under different situations when FSIR is used.
There are many artificial parameters when fuse infrared and visible images, to overcome the lack of detail in the fusion image because of the artifacts, a novel fusion algorithm for infrared and visible images that is based on different constraints in non-subsampled shearlet transform (NSST) domain is proposed. There are high bands and low bands of images that are decomposed by the NSST. After analyzing the characters of the bands, fusing the high level bands by the gradient constraint, the fused image can obtain more details; fusing the low bands by the constraint of saliency in the images, the targets are more salient. Before the inverse NSST, the Nash equilibrium is used to update the coefficient. The fused images and the quantitative results demonstrate that our method is more effective in reserving details and highlighting the targets when compared with other state-of-the-art methods.
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