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In this paper, we propose a novel image enhancement algorithm via anti-degraded model and L L -based variational retinex (AD- L L VR) for non-uniform illumination endoscopic images. Firstly, a haze-free endoscopic image is obtained by an anti-degraded model named dark channel prior (DCP). For getting a more accurate transmission map, it is refined by using a guided image filtering. Secondly, the haze-free endoscopic image is decomposed into detail and naturalness components by light filtering. Thirdly, a logarithmic Laplacian-based gamma correction (LLGC) is added to the naturalness component for preventing color cast and uneven lighting. Fourthly, we assume that the error between the detail component of the haze-free image and the product of associated reflectance and background illumination follows Gaussian-Laplacian distribution. So, the associated reflectance component can be obtained by using the proposed L L -based variational retinex ( L L VR) model. Finally, the recombination of modified naturalness component and associated reflectance component become the final result. Experimental results demonstrate that the proposed algorithm reveals more details in the background regions as well as other interesting areas and can mostly prevent the color cast. It has a better performance on increasing diagnosis and reducing misdiagnosis than other existing enhancement methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel incremental subspace and probability mask constrained sparse representation model in Smart and Autonomous Systems (SAS). In contrast to traditional sparse representation based tracking methods, the proposed model uses the trivial templates to model both reconstruction errors caused by sparse representation and the Eigen subspace representation. Besides, to alleviate the fact that the trivial templates can be activated to represent any image patch, we further propose to constrain the trivial templates with a probability mask. A unified objective function is proposed and a customized APG method is developed to effectively solve the optimization problem. In addition, a robust observation likelihood metric is proposed and a Bayes rule based color histogram model is proposed to construct the probability mask. Numerous qualitative and quantitative evaluations demonstrate that our tracker outperforms the state-of-the-art trackers in a wide range of tracking scenarios. [ABSTRACT FROM AUTHOR]