Although existing 3D super-resolution methods for magnetic resonance imaging (MRI) volumetric data can provide better visual images than some traditional 2D methods, they should face challenge of increasing network's parameters and computing cost for getting higher reconstruction accuracy. To address this issue, a lightweight 3D multi scale distillation volumetric Transformer, named Transformer-based dual-attention feature distillation (TDAFD) network, is proposed for 3D MRI by utilizing 3D information hiding in images sufficiently. Our TDAFD network contains several proposed dual-attention feature distillation (DAFD) modules and two designed recursive volumetric Transformers (RVT). Concretely, the proposed DAFD module contains a multi-scale feature distillation (MSFD) block for extracting global features under different scales and a feature enhancement dual attention block (FEDAB) for concentrating on the key features better. In addition, our RVT develops 2D Transformer to 3D and save network's parameters via recursion operations for capturing long-term dependencies in volumetric images effectively. Therefore, our proposed TDAFD network can not only extract deeper features via multi scale feature distillation and Transformer, but also realize the balance of performances and network's parameters. Extensive experiments illustrate that our proposed method achieves superior reconstruction performances than some popular 3D MRI SR methods, and saves number of weights and FLOPs.

Abstract Discriminative correlation filter is one of the quick and effective ways for studying visual tracking. However, discriminative correlation filter‐based methods still suffer from many challenging questions caused by environmental interferences, such as spatial boundary effect, temporal filter degradation, and tracking drift. A novel appearance optimisation model, named spatial and long–short temporal attention model, has been proposed based on a new spatial regularisation term and a long–short temporal regularisation term for learning the correlation filter to localise the target. On the one hand, our proposed method can improve the classical spatial regularisation term with a new weight matrix to alleviate the spatial boundary effect. On the other hand, two new temporal regularisation terms are designed: a short temporal regularisation term and a long temporal regularisation term. The short temporal regularisation term can enlarge the inner connections of the current frame and all foregoing frames to improve the tracking performances, and the long temporal regularisation term can address the influence of occlusion by using the similarity between the initial filter and the current one. Extensive experiments on various benchmarks illustrate that our proposed tracker performs favourably against several related popular trackers.

Computational efficiency is critical to many mobile and always-on face recognition applications. To this end, a heterogeneous spiking neural network (SNN) is proposed for face recognition. To obtain high recognition accuracy at minimal computational overheads, the heterogeneous SNN consists of an encoding subnet for sparse image feature encoding and classification subnet for feature classification. The experimental results suggest that the proposed heterogeneous algorithm can achieve high recognition accuracy on small datasets of human face samples with labeled identities at a high computational efficiency with very low neuronal activities. The proposed SNN is promising for low-cost mobile or always-on systems with strictly constrained resource and energy budgets.

Abstract Recently, there have been many visual tracking methods based on correlation filters. These methods mainly enhance the tracking performances by considering the information of background, space, or time in the appearance model. This paper proposes an effective tracking method, named adaptive spatial–temporal regularized correlation filter (ASTRCF) tracker, based on the popular adaptive spatially regularized correlation filter (ASRCF) tracker. That is, the continuity of object's motion in the process of tracking is considered by introducing a temporal‐regularized term in the appearance model of ASRCF tracker. Furthermore, its solution is inferred by applying the alternating direction method of multipliers. The proposed appearance model contains a background‐awareness term, a spatially regularized term, an adaptive‐weight term, and a temporal‐regularized term. Therefore, it can not only keep the good performances of ASRCF tracker, such as learning the background information and the spatial information adaptively to enhance the discriminating ability, but also take advantage of the relation of correlation filters in the last frame and the current frame for addressing the complex cases, such as occlusion, and fast motion. Extensive experimental results on various challenging databases show that the proposed ASTRCF tracker achieves better tracking performances than some state‐of‐the‐art trackers.

Image super‐resolution (SR) reconstruction, which gains high‐pixel and multi‐detail image from single or several low‐pixel images, has attracted increasing interest in recent years. This study proposes a new SR method based on sparse representation, which made good use of the non‐local (NL) structure similarity and edge sharpness dictionary. Firstly, all the training patches are classified into different clusters according to diverse edge sharpness of patches. Secondly, different dictionaries are trained for different training patches in each cluster. Thirdly, the NL structure similarity is added into the constraint of NL structure similarity model, and the suitable dictionary is selected for current patch to achieve the coefficients according to the value of edge sharpness of patch. Finally, the high‐resolution (HR) image is obtained by integrating HR patches obtained by the product of HR dictionaries and coefficients. Moreover, by calculating edge sharpness, the different dictionaries which adapt to patches with different structure are obtained, and the NL similarity is well utilised and more details are added to HR patch. Compared to some classical and common methods, the proposed method possesses better reconstruction effects in numerical and visual aspects.