The misty, foggy, or hazy weather conditions lead to image color distortion and reduce the resolution and the contrast of the observed object in outdoor scene acquisition. In order to detect and remove haze, this article proposes a novel effective algorithm for visibility enhancement from a single gray or color image. Since it can be considered that the haze mainly concentrates in one component of the multilayer image, the haze-free image is reconstructed through haze layer estimation based on the image filtering approach using both low-rank technique and the overlap averaging scheme. By using parallel analysis with Monte Carlo simulation from the coarse atmospheric veil by the median filter, the refined smooth haze layer is acquired with both less texture and retaining depth changes. With the dark channel prior, the normalized transmission coefficient is calculated to restore fogless image. Experimental results show that the proposed algorithm is a simpler and efficient method for clarity improvement and contrast enhancement from a single foggy image. Moreover, it can be comparable with the state-of-the-art methods, and even has better results than them.

An improved dehazing algorithm based on dark channel theory is proposed, in order to solve the problems of colour distortion and halo effect which still exists in dark channel prior algorithm. The dark channel prior theory may lead to colour distortion in sky region. Firstly, the guided filter is used to refine the segmentation of the sky region, and the atmospheric light is estimated accurately. Then, the median filter is used to obtain the detailed edge information. So a more clear transmission can be gotten which effectively suppress the halo problem. Finally, the gamma correction is applied to enhance image lightness with an empirically selected gamma parameter. The experimental results show that the proposed algorithm can effectively remove the haze. It can correct the colour distortion of the sky area and eliminate the halo effect at the edge of the scene.

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Over the last decade, the surface plasmon resonance (SPR) biosensor technique has received a great research interest. The major advantages of these sensors are its fast response and being able to detect the multi-analytes at one time. And its application areas include the detection of the biological analytes and the analysis of the biomolecular interactions where SPR biosensors provide with the benefits of the label-free real-time analytical technology. The general principles of SPR sensor action and the major SPR formats are briefly described in this paper, and the different commercialization of surface plasmon resonance sensor technology is presented, then the recent advances in the development and the applications of SPR biosensors are discussed. Finally the future prospects of SPR sensor technology are prospected.

Vision-based lane-detection methods provide low-cost density information about roads for autonomous vehicles. In this paper, we propose a robust and efficient method to expand the application of these methods to cover low-speed environments. First, the reliable region near the vehicle is initialized and a series of rectangular detection regions are dynamically constructed along the road. Then, an improved symmetrical local threshold edge extraction is introduced to extract the edge points of the lane markings based on accurate marking width limitations. In order to meet real-time requirements, a novel Bresenham line voting space is proposed to improve the process of line segment detection. Combined with straight lines, polylines, and curves, the proposed geometric fitting method has the ability to adapt to various road shapes. Finally, different status vectors and Kalman filter transfer matrices are used to track the key points of the linear and nonlinear parts of the lane. The proposed method was tested on a public database and our autonomous platform. The experimental results show that the method is robust and efficient and can meet the real-time requirements of autonomous vehicles.