Single image Dehazing has become a challenging task for a variety of image processing and computer applications. Many attempts have been devised to recover faded colors and improve image contrast. Such methods, however, do not achieve maximum restoration, as images are often subject to color distortion. This paper proposes an efficient single image Dehazing algorithm that offers satisfactory scene radiance restoration. The proposed method stands on the estimation of two key indices; image blur and atmospheric light that can be employed in the Image Formation Model (IFM) to recover scene radiance of the hazy image. More clearly, we propose an efficient depth estimation method using image blur. Most existing algorithms implement atmospheric light as a constant which often leads to inaccurate estimations, we propose a new algorithm "A-Estimate" based on blur and energy to estimate the atmospheric light accurately, an adaptive transmission map also has been proposed. Experimental results on real and synthesized hazy images demonstrate an improved performance in the proposed method when compared to existing state-of-the-art methods.
This paper discusses the iterative learning control problem for a class of non-linear partial difference system hyperbolic types. The proposed algorithm is the PD-type iterative learning control algorithm with initial state learning. Initially, we introduced the hyperbolic system and the control law used. Subsequently, we presented some dilemmas. Then, sufficient conditions for monotone convergence of the tracking error are established under the convenient assumption. Furthermore, we give a detailed convergence analysis based on previously given lemmas and the discrete Gronwall’s inequality for the system. Finally, we illustrate the effectiveness of the method using a numerical example.
This paper proposes a partial differential equation model based on the model introduced by V. A. Kuznetsov and M. A. Taylor, which explains the dynamics of a tumor–immune interaction system, where the immune reactions are described by a Michaelis–Menten function. In this work, time delay and diffusion process are considered in order to make the studied model closer to reality. Firstly, we analyze the local stability of equilibria and the existence of Hopf bifurcation by using the delay as a bifurcation parameter. Secondly, we use the normal form theory and the center manifold reduction to determine the normal form of Hopf bifurcation for the studied model. Finally, some numerical simulations are provided to illustrate the analytic results. We show how diffusion has a significant effect on the dynamics of the delayed interaction tumor–immune system.
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