Speckle Variance Photoacoustic Microscopy for Microhemodynamic Imaging.

Relying on the strong optical absorption of hemoglobin to pulsed laser energy, photoacoustic microscopy provides morphological and functional information on microvasculature label-freely. Here, we propose speckle variance photoacoustic microscopy (SV-PAM), which harnesses intrinsic imaging contrast from temporal-varied photoacoustic signals of moving red blood cells in blood vessels, for recovering three-dimension hemodynamic images down to capillary-level resolution within the microcirculatory tissue beds . Calculating the speckle variance of consecutive photoacoustic B-scan frames acquired at the same lateral position enables accurate identification of blood perfusion and occlusion, which provides interpretations of dynamic blood flow in the microvasculature, in addition to the microvascular anatomic structures.

Enhanced tensor low-rank representation learning for multi-view clustering.

Multi-view subspace clustering (MSC), assuming the multi-view data are generated from a latent subspace, has attracted considerable attention in multi-view clustering. To recover the underlying subspace structure, a successful approach adopted recently is subspace clustering based on tensor nuclear norm (TNN). But there are some limitations to this approach that the existing TNN-based methods usually fail to exploit the intrinsic cluster structure and high-order correlations well, which leads to limited clustering performance.

Multiple graphs learning with a new weighted tensor nuclear norm.

As an effective convex relaxation of the rank minimization model, the tensor nuclear norm minimization based multi-view clustering methods have been attracting more and more interest in recent years. However, most existing clustering methods regularize each singular value equally, restricting their capability and flexibility in tackling many practical problems, where the singular values should be treated differently. To address this problem, we propose a novel weighted tensor nuclear norm minimization (WTNNM) based method for multi-view spectral clustering.

Enhanced Tensor RPCA and its Application.

Despite the promising results, tensor robust principal component analysis (TRPCA), which aims to recover underlying low-rank structure of clean tensor data corrupted with noise/outliers by shrinking all singular values equally, cannot well preserve the salient content of image. The major reason is that, in real applications, there is a salient difference information between all singular values of a tensor image, and the larger singular values are generally associated with some salient parts in the image. Thus, the singular values should be treated differently.

Adaptive latent similarity learning for multi-view clustering.

Most existing clustering methods employ the original multi-view data as input to learn the similarity matrix which characterizes the underlying cluster structure shared by multiple views. This reduces the flexibility of multi-view clustering methods due to the fact that multi-view data usually contains noise or the variation between multi-view data points, which should belong to the same cluster, is larger than the variation between data points belonging to different clusters. To address these problems, we propose a novel multi-view clustering model, namely adaptive latent similarity learning (ALSL) for multi-view clustering.

In vivo blind-deconvolution photoacoustic ophthalmoscopy with total variation regularization.

Photoacoustic ophthalmoscopy (PAOM) is capable of noninvasively imaging anatomic and functional information of the retina in living rodents. However, the strong ocular aberration in rodent eyes and limited ultrasonic detection sensitivity affect PAOM's spatial resolution and signal-to-noise ratio (SNR) in in vivo eyes. In this work, we report a computational approach to combine blind deconvolution (BD) algorithm with a regularizing constraint based on total variation (BDTV) for PAOM imaging restoration.

F-norm distance metric based robust 2DPCA and face recognition.

Two-dimensional principal component analysis (2DPCA) employs squared F-norm as the distance metric for dimensionality reduction. It is commonly known that squared F-norm is sensitive to the presence of outliers. To address this problem, we use F-norm instead of squared F-norm as the distance metric in the objective function and develop a non-greedy algorithm, which has a closed-form solution in each iteration and can maximize the criterion function, to solve the optimal solution.

Robust DLPP With Nongreedy $\ell _1$ -Norm Minimization and Maximization.

Recently, discriminant locality preserving projection based on L1-norm (DLPP-L1) was developed for robust subspace learning and image classification. It obtains projection vectors by greedy strategy, i.e.