Multiplicative speckle noise removal is a challenging task in image processing. Motivated by the performance of anisotropic diffusion in additive noise removal and the structure of the standard deviation of a compressed speckle noisy image, we address this problem with anisotropic diffusion theories. Firstly, an anisotropic diffusion model based on image statistics, including information on the gradient of the image, gray levels, and noise standard deviation of the image, is proposed. Although the proposed model can effectively remove multiplicative speckle noise, it does not consider the noise at the edge during the denoising process. Hence, we decompose the divergence term in order to make the diffusion at the edge occur along the boundaries rather than perpendicular to the boundaries, and improve the model to meet our requirements. Secondly, the iteration stopping criteria based on kurtosis and correlation in view of the lack of ground truth in real image experiments, is proposed. The optimal values of the parameters in the model are obtained by learning. To improve the denoising effect, post-processing is performed. Finally, the simulation results show that the proposed model can effectively remove the speckle noise and retain minute details of the images for the real ultrasound and RGB color images.

Most existing trackers perform well in the occurrence of short-term occlusions and appearance and illumination variations, but struggle with the challenges of long-term tracking which include heavy or long-term occlusions, and out-of-view objects. We propose a spatial-temporal aware long-term object tracking method in this paper, to manage the challenges of long term tracking. Firstly, we present a spatial-temporal aware correlation filter, which jointly models the spatial and temporal information of the target within the correlation filter framework to locate a visible target from frame to frame. Then, we design a tracking uncertainty detection mechanism to activate the re-detector in case of track failure. The mechanism relies on the variations of correlation response measured spatially, and the appearance similarity estimated temporally. Finally, we propose a spatial-temporal aware long-term re-detector to recover the target when it becomes visible. In the re-detection process, a large number of candidates are sampled, evaluated, and refined spatially to obtain accurate locations. Additionally, reliable memory retained through conservative template updating enables the recovery of the target by similarity matching. The spatial-temporal information is explicitly encoded in each component, which operates collaboratively to boost the overall performance. Extensive experimental results conducted on publicly available benchmark datasets demonstrate that the proposed method performs favorably when compared to other state-of-the-art trackers.

This study presents a robust object tracking method based on occlusion detection via correlation filters. In the proposed method, multi‐feature kernelised correlation filter is employed to estimate the preliminary location of the tracked target. To predict target occlusion state, the intrinsic relationship between the most reliable tracked target and its context information is exploited via correlation filters, together with a response stability constraint to make the detection more reliable. A long‐term filter is activated to recover the target if the occlusion occurs. Furthermore, the model is updated adaptively based on the changes of occlusion state and target appearance to make the tracking process robust. Extensive experimental results demonstrate that the proposed tracking method with occlusion detection performs favourably against 15 state‐of‐the‐art trackers over 100 challenge sequences on the object tracking benchmark OTB‐2015.

Visual object tracking, one of the most fundamental and challenging tasks with several applications, has attracted a great deal of research interest and has been widely applied in unmanned aerial vehicle (UAV) missions. Despite the efficient correlation filter (CF)-based approaches that have shown prominent performance in visual object tracking for different UAV applications, the model drift, caused by undesired boundary effects, filter degradation, and severe appearance variations, remains a challenging problem. To solve the aforementioned issue, we propose saliency-enhanced background-aware CFs with dual temporal regularization for UAV tracking. In more detail, the dual temporal regularization term is introduced to reinforce inter-frame continuity using current and previous frames’ filter and sample information, respectively. As for the saliency enhancement term, it is integrated by multiplying the merged saliency map and the vectorized image patch element-wise to emphasize the noticeable object and suppress irrelevant background noise. Finally, a CF regression model is formulated and its closed-form solution is derived by utilizing the alternating direction method of multipliers (ADMM) algorithm. The saliency and temporal information in the tracking scene are fully considered to promote model discriminative ability and boost the overall tracking performance. Extensive experiments are conducted on three public UAV benchmark datasets UAV123@10 fps, DTB70, and UAVDT using 243 challenging sequences. The results demonstrate that the proposed method performs favorably against other state-of-the-art trackers while operating at a real-time speed on a single CPU, making it suitable for UAV tracking tasks.

Correlation filter-based trackers have made significant progress in visual object tracking for various types of unmanned aerial vehicle (UAV) applications due to their promising performance and efficiency. However, the boundary effect remains a challenging problem. Several methods enlarge search areas to handle this shortcoming but introduce more background noise, and the filter is prone to learn from distractors. To address this issue, we present spatial regularized correlation filters with response consistency and distractor repression. Specifically, a temporal constraint is introduced to reinforce the consistency across frames by minimizing the difference between consecutive correlation response maps. A dynamic spatial constraint is also integrated by exploiting the local maximum points of the correlation response produced during the detection phase to mitigate the interference from background distractions. The proposed appearance model can optimize the temporal and spatial constraints together with a spatial regularization weight simultaneously. Meanwhile, the proposed appearance model can be solved effectively based on the alternating direction method of multipliers algorithm. The spatial and temporal information concealed in the response maps is fully taken into consideration to boost overall tracking performance. Extensive experiments are conducted on a public UAV benchmark dataset with 123 challenging sequences. The experimental results and analysis demonstrate that the proposed method outperforms 12 state-of-the-art trackers in terms of both accuracy and robustness while efficiently operating in real time.