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In this Special Section of the IEEE Transactions on Circuits and Systems for Video Technology, it is our honor to present emerging advanced machine learning and data analytics algorithms aiming at catalyzing synergies among image/video processing, text/speech understanding, and multimodal learning in biomedical informatics. Our goals are to 1) introduce novel data-driven models to accelerate knowledge discovery in biomedicine through the seamless integration of medical data collected from imaging systems, laboratory and wearable devices, as well as other related medical devices; 2) promote the development of new multi-modal learning systems to enhance the healthcare quality and patient safety; and 3) promote new applications in biomedical informatics that can leverage or benefits from the integration of multi-modal data and machine learning. [ABSTRACT FROM AUTHOR]

The thresholded feature has recently emerged as an extremely efficient, yet rough empirical approximation, of the time-consuming sparse coding inference process. Such an approximation has not yet been rigorously examined, and standard dictionaries often lead to non-optimal performance when used for computing thresholded features. In this paper, we first present two theoretical recovery guarantees for the thresholded feature to exactly recover the nonzero support of the sparse code. Motivated by them, we then formulate the Dictionary Learning for Thresholded Features (DLTF) model, which learns an optimized dictionary for applying the thresholded feature. In particular, for the $(k, 2)$ norm involved, a novel proximal operator with log-linear time complexity $O(m\log m)$ is derived. We evaluate the performance of DLTF on a vast range of synthetic and real-data tasks, where DLTF demonstrates remarkable efficiency, effectiveness, and robustness in all experiments. In addition, we briefly discuss the potential link between DLTF and deep learning building blocks. [ABSTRACT FROM AUTHOR]

In old video restoration, automatic detection of common defects, e.g., scratches and blotches, has always been emphasized. While prior thoughts mainly focus on detecting blotches and linear, vertical scratches separately, this paper contributes to a more generalized and challenging issue: simultaneous detection of blotches and complex scratches in video, with much less knowledge of them. We investigate the characteristics of blotches and scratches in space and time domain, and propose a novel detection method based on two main steps: cartoon-texture decomposition in the space domain and content-defect separation in the time domain. We then formulate it into convex optimization problems and develop corresponding algorithms. The experiment results demonstrate that the proposed method is of high detection accuracy, verifying the effectiveness of our detection via a video decomposition method. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel multi-level frame interpolation scheme by exploiting the interactions among different levels. The proposed scheme includes three major stages that work at block level, pixel level, and sequence level, respectively. Effective algorithms are designed for each stage, i.e., block-level motion estimation with dropping unreliable motion vectors, pixel-level motion vector-guided partial scale-invariant feature transform flow matching, and sequence-level 3-D total variation regularized completion. Compared to traditional methods that focus mostly at one single level, the proposed scheme manages to recognize and utilize the interactions among the three levels based on their distinct characteristics and intertwined relationships. With a proper exploitation of interactions, unique advantages for each level can be effectively preserved while inherent limitations of a given level can be overcome by utilizing information from other levels. Extensive experiments have confirmed its superior performance over several classical schemes, in both subjective visual quality and objective peak signal-to-noise ratio/structure similarity measurements, and typical artifacts can be significantly reduced. [ABSTRACT FROM AUTHOR]

In this paper, we propose a robust temporal-spatial decomposition (RTSD) model and discuss its applications in video processing. A video sequence usually possesses high correlations among and within its frames. Fully exploiting the temporal and spatial correlations enables efficient processing and better understanding of the video sequence. Considering that the video sequence typically contains slowly changing background and rapidly changing foreground as well as noise, we propose to decompose the video frames into three parts: the temporal-spatially correlated part, the feature compensation part, and the sparse noise part. Accordingly, the decomposition problem can be formulated as the minimization of a convex function, which consists of a nuclear norm, a total variation (TV)-like norm, and an \ell1 norm. Since the minimization is nontrivial to handle, we develop a two-stage strategy to solve this decomposition problem, and discuss different alternatives to fulfil each stage of decomposition. The RTSD model treats video frames as a unity from both the temporal and spatial point of view, and demonstrates robustness to noise and certain background variations. Experiments on video denoising and scratch detection applications verify the effectiveness of the proposed RTSD model and the developed algorithms. [ABSTRACT FROM PUBLISHER]