Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

Modern Statistical/Machine Learning Techniques for Bio/Neuro-imaging Applications

Developments in modern bio-imaging techniques have allowed the routine collection of a vast amount of data from various techniques. The challenges lie in how to build accurate and efficient models to draw conclusions from the data and facilitate scientific discoveries. Fortunately, recent advances in statistics, machine learning, and deep learning provide valuable tools. This thesis describes some of our efforts to build scalable Bayesian models for four bio-imaging applications: (1) Stochastic Optical Reconstruction Microscopy (STORM) Imaging, (2) particle tracking, (3) voltage smoothing, (4) detect color-labeled neurons in c elegans and assign identity to the detections

Emergence of charge order in a staggered loop-current phase of cuprate high-temperature superconductors

We study the emergence of charge ordered phases within a pi-loop current (piLC) model for the pseudogap based on a three-band model for underdoped cuprate superconductors. Loop currents and charge ordering are driven by distinct components of the short-range Coulomb interactions: loop currents result from the repulsion between nearest-neighbor copper and oxygen orbitals, while charge order results from repulsion between neighboring oxygen orbitals. We find that the leading piLC phase has an antiferromagnetic pattern similar to previously discovered staggered flux phases, and that it emerges abruptly at hole dopings p below the van Hove filling. Subsequent charge ordering tendencies in the piLC phase reveal that diagonal d-charge density waves (dCDW) are suppressed by the loop currents while axial order competes more weakly. In some cases we find a wide temperature range below the loop-current transition, over which the susceptibility towards an axial dCDW is large. In these cases, short-range axial charge order may be induced by doping-related disorder. A unique feature of the coexisting dCDW and piLC phases is the emergence of an incommensurate modulation of the loop currents. If the dCDW is biaxial (checkerboard) then the resulting incommensurate current pattern breaks all mirror and time-reversal symmetries, thereby allowing for a polar Kerr effect

Semantic-Aware Dual Contrastive Learning for Multi-label Image Classification

Extracting image semantics effectively and assigning corresponding labels to multiple objects or attributes for natural images is challenging due to the complex scene contents and confusing label dependencies. Recent works have focused on modeling label relationships with graph and understanding object regions using class activation maps (CAM). However, these methods ignore the complex intra- and inter-category relationships among specific semantic features, and CAM is prone to generate noisy information. To this end, we propose a novel semantic-aware dual contrastive learning framework that incorporates sample-to-sample contrastive learning (SSCL) as well as prototype-to-sample contrastive learning (PSCL). Specifically, we leverage semantic-aware representation learning to extract category-related local discriminative features and construct category prototypes. Then based on SSCL, label-level visual representations of the same category are aggregated together, and features belonging to distinct categories are separated. Meanwhile, we construct a novel PSCL module to narrow the distance between positive samples and category prototypes and push negative samples away from the corresponding category prototypes. Finally, the discriminative label-level features related to the image content are accurately captured by the joint training of the above three parts. Experiments on five challenging large-scale public datasets demonstrate that our proposed method is effective and outperforms the state-of-the-art methods. Code and supplementary materials are released on https://github.com/yu-gi-oh-leilei/SADCL.Comment: 8 pages, 6 figures, accepted by European Conference on Artificial Intelligence (2023 ECAI