Epilepsy is a brain network disorder that manifests through recurrent seizures. In cases of drug-resistant epilepsy, surgical removal of pivotal nodes within the epileptic brain network can lead to seizure freedom. Virtual resection on patient-specific brain network models can aid in the prediction of surgical outcomes. Some studies have investigated the virtual resection on undirected brain connectivity networks, such as using Pearson correlation or structural connectivity. We hypothesize that the directed functional connectivity enhances prediction performance. This study proposes a new approach for surgical outcome prediction by applying virtual resection of autoregressive (AR) connectivity networks in epilepsy patients. Intracranial EEG recordings from 16 drug-resistant epilepsy patients were analyzed. The performance of the proposed approach was evaluated based on patients' surgical volumes and prognosis outcome. We compared the performance of the AR connectivity with six other measures and concurrently explored three distinct neural mass models. The results show that virtual resection on AR connectivity demonstrated predictive accuracy at 87.5% when paired with the bistable neural mass model. Notably, all eight patients with poor outcomes were accurately identified. In addition, our data shows that the estimated epileptic network is relatively stable during the interictal interval. Leveraging the AR model results in estimated directional connectivity among epileptic brain regions, which can then be used effectively for virtual resection. Our approach offers a promising avenue for clinicians in preoperative evaluation and augments existing clinical methodologies.

Tracking low-resolution (LR) targets is a practical yet quite challenging problem in real video analysis applications. Lack of discriminative details in the visual appearance of the LR target leads to the matching ambiguity, which confronts most existing tracking methods. Although artificially enhancing the video resolution by superresolution (SR) techniques before analyzing might be an option, the high demand of computational cost can hardly meet the requirements of the tracking scenario. This paper presents a novel solution to track LR targets without explicitly performing SR. This new approach is based on discriminative metric preservation that preserves the data affinity structure in the high-resolution (HR) feature space for effective and efficient matching of LR images. In addition, we substantialize this new approach in a solid case study of differential tracking under metric preservation and derive a closed-form solution to motion estimation for LR video. In addition, this paper extends the basic linear metric preservation method to a more powerful nonlinear kernel metric preservation method. Such a solution to LR target tracking is discriminative, robust, and efficient. Extensive experiments validate the entrustments and effectiveness of the proposed approach and demonstrate the improved performance of the proposed method in tracking LR targets.

Super-resolution technology provides an effective way to increase image resolution by incorporating additional information from successive input images or training samples. Various super-resolution algorithms have been proposed based on different assumptions, and their relative performances can differ in regions of different characteristics within a single image. Based on this observation, an adaptive algorithm is proposed in this paper to integrate a higher level image classification task and a lower level super-resolution process, in which we incorporate reconstruction-based super-resolution algorithms, single-image enhancement, and image/video classification into a single comprehensive framework. The target high-resolution image plane is divided into adaptive-sized blocks, and different suitable super-resolution algorithms are automatically selected for the blocks. Then, a deblocking process is applied to reduce block edge artifacts. A new benchmark is also utilized to measure the performance of super-resolution algorithms. Experimental results with real-life videos indicate encouraging improvements with our method.

Super-resolution is a widely applied technique that improves the resolution of input images by software methods. Most conventional reconstruction-based super-resolution algorithms assume accurate dense optical flow fields between the input frames, and their performance degrades rapidly when the motion estimation result is not accurate enough. However, optical flow estimation is usually difficult, particularly when complicated motion is presented in real-world videos. In this paper, we explore a new way to solve this problem by using sparse feature point correspondences between the input images. The feature point correspondences, which are obtained by matching a set of feature points, are usually precise and much more robust than dense optical flow fields. This is because the feature points represent well-selected significant locations in the image, and performing matching on the feature point set is usually very accurate. In order to utilize the sparse correspondences in conventional super-resolution, we extract an adaptive support region with a reliable local flow field from each corresponding feature point pair. The normalized prior is also proposed to increase the visual consistency of the reconstructed result. Extensive experiments on real data were carried out, and results show that the proposed algorithm produces high-resolution images with better quality, particularly in the presence of large-scale or complicated motion fields.

Tracking low resolution (LR) targets is a practical yet quite challenging problem in real applications. The loss of discriminative details in the visual appearance of the L-R targets confronts most existing visual tracking methods. Although the resolution of the LR video inputs may be enhanced by super resolution (SR) techniques, the large computational cost for high-quality SR does not make it an attractive option. This paper presents a novel solution to track LR targets without performing explicit SR. This new approach is based on discriminative metric preservation that preserves the structure in the high resolution feature space for LR matching. In addition, we integrate metric preservation with differential tracking to derive a closed-form solution to motion estimation for LR video. Extensive experiments have demonstrated the effectiveness and efficiency of the proposed approach.

Video super-resolution can be generally divided into two categories: incremental video super-resolution and simultaneous video super-resolution. Incremental video super-resolution algorithms are usually faster, but their results cannot be guaranteed to be visually consistent to the human vision system. An adaptive incremental video super-resolution framework with the temporal consistency constraint is proposed in this paper. The temporal consistency among the video frames is enforced by imposing the similarity between the adjacent reconstructed HR frames. The variances of the potential functions, which affect the weights of the different terms in the utility function, are adaptively determined so that the algorithm is robust to various motion and image content situations. Some considerations, such as the incremental motion estimation, are also incorporated to improve the efficiency of the algorithm, which makes the proposed algorithm near-realtime. The experimental results show that the proposed algorithm can generate HR video with high quality while saving the computational time as well.

In this paper a novel practical and adaptive framework for super-resolution is proposed. Existing super-resolution algorithms are limited by several assumptions, fit different imaging environments; hence they may fail when facing real complex scenes. We propose to divide the target image into adaptive-sized blocks, and apply different conventional algorithms to different parts of the image with different characteristics. The proposed framework is of high extensibility and generality; various super-resolution and single frame im age enhancement technologies can be adaptively assembled to improve the robustness and result quality of SR operation. Experiments with real-life video indicate encouraging improvements.