Spectral videos (SVs) capture spatio-temporal-spectral information from dynamic scenes, but their acquisition traditionally requires expensive and complex systems, motivating the development of compressive spectral video sensing (CSVS). It typically employs the coded aperture snapshot spectral imager (CASSI) to acquire compressed measurements, from which SVs are reconstructed via model-driven or learning-based algorithms. However, two major limitations remain in current CASSI-based reconstruction methods: 1) conventional model-driven algorithms rely on iterative optimization, which limits their representational capacity in complex scenes and results in slow reconstruction; 2) existing deep learning-based approaches overlook the joint modeling of spatial, temporal, and spectral correlations, failing to fully exploit the multi-dimensional dependencies. Hence, we propose a principled compressive spectral video sensing unfolding framework (CSVSUF) in a CASSI system for spectral video reconstruction. Moreover, we develop a novel spatio-temporal-spectral prior-learning Transformer (STS-PLT) to capture the multi-dimensional correlations within each unfolding stage. By treating STS-PLT as a Gaussian denoiser for the prior term in CSVSUF, we establish a deep unfolding-based method for CSVS. Extensive experiments demonstrate that our method consistently outperforms existing approaches in both reconstruction accuracy and visual quality, validating the benefit of combining physics-guided modeling with deep prior learning in CSVS. Code is available at https://github.com/zli1024/CSVSUF.

To accelerate the data acquisition speed of magnetic resonance imaging (MRI) and improve the reconstructed MR images’ quality, we propose a parallel MRI reconstruction model (SPIRiT‐Net), which combines the iterative self‐consistent parallel imaging reconstruction model (SPIRiT) with the cascaded complex convolutional neural networks (CCNNs). More specifically, this model adopts the SPIRiT model for reconstruction in the k‐space domain and the cascaded CCNNs with dense connection for reconstruction in the image domain. Meanwhile, this model introduces the data consistency layers for better reconstruction in both the image domain and the k‐space domain. The experimental results on two clinical knee datasets as well as the fastMRI brain dataset under different undersampling patterns show that the SPIRiT‐Net model achieves better reconstruction performance in terms of visual effect, peak signal‐to‐noise ratio, and structural similarity over SPIRiT, Deepcomplex, and DONet. It will be beneficial to the diagnosis of clinical medicine.

Alzheimer's Disease (AD) is a neurodegenerative disorder that causes a continuous decline in cognitive functions and eventually results in death. An early AD diagnosis is important for taking active measures to slow its deterioration. Traditional diagnoses are usually based on clinical experience, which is limited by several realistic factors. In this paper, we focus on exploiting deep learning techniques to diagnose AD based on eye-tracking behaviors. Visual attention, as a typical eye-tracking behavior, is of great clinical value in detecting cognitive abnormalities in AD patients. To better analyze the differences in visual attention between AD patients and normals, we first conducted a 3D comprehensive visual task on a noninvasive eye-tracking system to collect visual attention heatmaps. Then a multilayered comparison convolutional neural network (MC-CNN) is proposed to distinguish the visual attention differences between AD patients and normals. In MC-CNN, the multilayered feature representations of heatmaps were obtained by hierarchical residual blocks to better encode eye movement behaviors, which were further integrated into a distance vector to benefit the comprehensive visual task. From evaluation, MC-CNN can distinguish AD patients from normals with 0.84 accuracy, 0.86 recall, 0.82 precision, 0.83 F1-score and 0.90 area under the curve (AUC). The above results demonstrate the effectiveness of the proposed MC-CNN in AD diagnosis based on the comprehensive 3D visual task.

Abstract Eigenvector‐based SPIRiT (ESPIRiT) can estimate multiple sets of coil sensitivity maps from the calibration matrix constructed from the auto‐calibration data. Recently, the L1 norm and total variation were combined with the ESPIRiT model to improve the reconstruction quality of magnetic resonance (MR) images. To further improve the reconstruction performance, the non‐local low‐rank regularisation term is incorporated into the ESPIRiT model (NLR‐ESPIRiT) is proposed. The proposed NLR‐ESPIRiT model takes full advantage of the non‐local self‐similarity features of MR images. The resulting optimisation problem can be transformed into a gradient problem and a denoising problem with low‐rank constraints using the operator splitting technique. The weighted nuclear norm (WNN) is applied as a surrogate of the rank. Then the denoising subproblem with the WNN can be effectively solved by using the alternating direction method of multipliers technique. For practical applications, a parameter‐selecting method is proposed to obtain almost optimal parameters for the same kind of MR images. Simulation experiments on in vivo data sets demonstrate that the proposed NLR‐ESPIRiT outperforms all competing traditional model‐based algorithms in terms of three objective metrics and visual comparison.

In practical multimedia systems, the content of coded video streams often needs to be re-edited at the nodes of transmitting networks. For example, logo insertion is always required for copyright protection at different local transmitting nodes. This kind of video stream editing is denoted as video content editing transcoding (VCET) in this paper. Though some techniques have been suggested for VCET, these methods cannot meet the requirement of dynamic transmitting bandwidth in practical applications. In this paper, we proposed an interface macroblock-based transcoding scheme for VCET, which can reuse the variable length codes of the original video streams as much as possible to achieve the best VCET quality. In order to ensure that the edited video streams can be transmitted by the original bandwidth, we also proposed a rate control algorithm for VCET, which can accurately control the bitrate of edited video streams according to the frame level coding bits of the original video streams. Experimental results showed that the proposed scheme achieved substantially better results in bitrate accuracy, computational complexities, and video quality than many other existing schemes.

In order to guarantee pay-TV services, data of digital television (DTV) programs are scrambled by conditional access systems (CAS). In practical applications, some DTV transmitting nodes need descramble the scrambled DTV programs for editing purposes. After editing, how to re-scramble these edited programs is a challenging task since building CAS at transmitting nodes is expensive and insecure. In this paper, we proposed a novel scheme to solve this problem. Together with the recommended scheme, we also proposed techniques regarding video key data selection and extraction, synchronization of scrambled and descrambled transport stream (TS) packets, and scrambled/unscrambled interleaving multiplexer. The proposed scheme requires much lower complexity than existing methods while maintaining enough security for practical applications. Neither professional CAS equipment nor real-time common key (CK) transmitting is required by our re-scrambling scheme. Various experimental results demonstrated that the proposed re-scrambling scheme achieved superior performance in practical DTV systems, and it also obtained good compatibility with different CAS algorithms.

It is difficult to solve Magnetic Resonance (MR) image reconstruction problems with linear combinations of total variation and ℓ1norm regularization terms. In order to solve these compound regularization problems, we propose an efficient algorithm in this letter. The proposed algorithm adopts the Bregman iteration technique to convert the original constrained problem to a sequence of unconstrained problems, which are then solved using operator splitting and variable splitting techniques. Simulation experiments demonstrate that significant improvement of the quality of reconstructed images is achieved by the proposed algorithm when compared to previous methods.