In reality, the quality of an image is generally affected by haze. To obtain a well-quality image, removing haze is a hot issue on theory and application. This paper proposes a new algorithm to remove haze of hazy images. In the algorithm, first, the ambient illumination is estimated by a logarithmic guide filtering that can reserve the characteristics of the bright source areas and improve the dark source areas of the hazy image. Second, to overcome the defect of dark channel prior (DCP) and the over-brightness of the bright channel prior (BCP), two models with two parameters are introduced to improve the DCP and BCP, called multi-channel prior method. At the same time, a self-adaptive method is presented to compute the values of the two parameters. At last, based on the multi-channel prior, a self-adaptive method is proposed to compute the transmission mapping value. Further, four classes hazy images are employed to test the proposed method. The experimental results carried out on the public databases demonstrate that the proposed algorithm can outperform the current state-of-the-arts, including more effective defogging, clearer visibility and richer details.INDEX TERMS Remove haze, hazy image, logarithmic guide filtering, multi-channel prior.

This paper presents a linear program model with linear complementarity constraints (LPLCC) to solve traffic signal optimization problem. The objective function of the model is to obtain the minimization of total queue length with weight factors at the end of each cycle. Then, a combination algorithm based on the nonlinear least regression and sequence quadratic program (NLRSQP) is proposed, by which the local optimal solution can be obtained. Furthermore, four numerical experiments are proposed to study how to set the initial solution of the algorithm that can get a better local optimal solution more quickly. In particular, the results of numerical experiments show that: The model is effective for different arrival rates and weight factors; and the lower bound of the initial solution is, the better optimal solution can be obtained.

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Purpose This paper aims to propose a novel nature-inspired optimization algorithm, called whirlpool algorithm (WA), which imitates the physical phenomenon of whirlpool. Design/methodology/approach The idea of this algorithm stems from the fact that the whirlpool has a descent direction and a vertex. Findings WA is tested with two types of models: 29 typical mathematical optimization models and three engineering problems (tension/compression spring design, welded-beam design, pressure vessel design). Originality/value The results shown that the WA is vying compared to the state-of-art algorithms likewise conservative approaches.

In this paper, we consider an adaptive system for controlling green times at junction. For this adaptive system, we present a multi-objective optimization model, which is much easier to solve than some existing models. Furthermore, to solve the new model, we suggest an algorithm, called NLRMNSGA-II, which is based on the nonlinear least regression and a modified non-dominated sorting genetic algorithm. Our numerical experiments indicate that the NLRMNSGA-II is an efficient algorithm for the considered adaptive system.