In this paper, we propose a multi-view structural local subspace tracking algorithm based on sparse representation. We approximate the optimal state from three views: (1) the template view; (2) the PCA (principal component analysis) basis view; and (3) the target candidate view. Then we propose a unified objective function to integrate these three view problems together. The proposed model not only exploits the intrinsic relationship among target candidates and their local patches, but also takes advantages of both sparse representation and incremental subspace learning. The optimization problem can be well solved by the customized APG (accelerated proximal gradient) methods together with an iteration manner. Then, we propose an alignment-weighting average method to obtain the optimal state of the target. Furthermore, an occlusion detection strategy is proposed to accurately update the model. Both qualitative and quantitative evaluations demonstrate that our tracker outperforms the state-of-the-art trackers in a wide range of tracking scenarios.

In this paper, we propose a novel image enhancement algorithm via anti-degraded model and L 1 L 2 -based variational retinex (AD-L 1 L 2 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 1 L 2 -based variational retinex (L 1 L 2 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.

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In this letter, a surround suppression variational Retinex enhancement algorithm (SSVR) is proposed for non-uniform illumination images. Instead of a gradient module, a surround suppression mechanism is used to provide spatial information in order to constrain the total variation regularization strength of the illumination and reflectance. The proposed strategy preserves the boundary areas in the illumination so that halo artifacts are prevented. It also preserves textural details in the reflectance to prevent from illumination compression, which further contributes to the contrast enhancement in the resulting image. In addition, strong regularization strength is enforced to eliminate uneven intensities in the homogeneous areas. The split Bregman optimization algorithm is employed to solve the proposed model. Finally, after decomposition, a contrast gain is added to reflectance for contrast enhancement, and a Laplacianbased gamma correction is added to illumination for prevent color cast. The recombination of the modified reflectance and illumination become the final result. Experimental results demonstrate that the proposed SSVR algorithm performs better than other methods.

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Correlation filter-based trackers have recently shown excellent performance in terms of motion blur and illumination changes, but they are notoriously sensitive to deformation. It has been demonstrated that the combination of the correlation filter-based tracker and the color histogram-based tracker can alleviate the deformation and keep advantages of the correlation filter-based tracker. However, the most existing complementary tracking algorithms, which use fixed complementary weights, limit the performance of every sub tracker. This paper introduces an adaptive complementary tracker by online learning dynamic complementary weights. The strategy enables inappropriate sub tracker to be down-weighted while increasing the impact of suitable one. We jointly learn the sub trackers and their reliability weights by regression analysis of the corresponding historical tracking results. The robustness of the model also can be improved by training each sub tracker with the result of historical tracking. Finally, both qualitative and quantitative evaluations demonstrate that our tracker achieves the state-of-the-art results in a wide range of tracking scenarios.