The fusion of infrared and visible images may result in low contrast, which is unsuitable for observation by human eyes. Thus, we propose a contrast-enhanced fusion algorithm with nonsubsampled shearlet transform (NSST) frames, in which the NSST is first employed to decompose each of the source images into one low frequency sub-band and a series of high frequency sub-bands. To improve the fusion performance, we designed two measures for fusion of the low frequency and the high frequency: the low frequency is divided into salient and nonsalient regions in accordance with the human visual system to improve the global contrast by targeted fusion and the high frequency requires a local contrast fusion strategy. Finally, the merged sub-bands are constructed according to the selection principles, and the final fused image is produced by applying the inverse NSST on these merged sub-bands. Experimental results demonstrate the effectiveness and superiority of the proposed method over the state-of-the-art fusion methods in terms of both visual effect and objective evaluation results.
Model drift is an important reason for tracking failure. In this paper, multiple discriminative models with object proposals are used to improve the model discrimination for relieving this problem. Firstly, the target location and scale changing are captured by lots of high-quality object proposals, which are represented by deep convolutional features for target semantics. And then, through sharing a feature map obtained by a pre-trained network, ROI pooling is exploited to wrap the various sizes of object proposals into vectors of the same length, which are used to learn a discriminative model conveniently. Lastly, these historical snapshot vectors are trained by different lifetime models. Based on entropy decision mechanism, the bad model owing to model drift can be corrected by selecting the best discriminative model. This would improve the robustness of the tracker significantly. We extensively evaluate our tracker on two popular benchmarks, the OTB 2013 benchmark and UAV20L benchmark. On both benchmarks, our tracker achieves the best performance on precision and success rate compared with the state-of-the-art trackers.
Infrared images have shortcomings of background noise, few details, and fuzzy edges. Therefore, noise suppression and detail enhancement play crucial roles in the infrared image technology field. To effectively enhance details and eliminate noises, an infrared image processing algorithm based on multiscale feature prior is proposed. First, the maximum a posterior model estimating optimal free-noise results is constructed and discussed. Second, based on the extended 16 high-order differential operators and multiscale features, we propose a structure feature prior that is immune to noises and depicts infrared image features more precisely. Third, with the noise-suppressed image, the final image is enhanced by the improved multiscale unsharp mask algorithm, which enhances details and edges adaptively. Finally, testing infrared images in different signal-to-noise ratio scenes, the effectiveness and robustness of the proposed approach is analyzed. Compared with other well-established methods, the proposed method shows the evident performance in terms of noise suppression and edge enhancement.