Tieyong Zeng

Tieyong Zeng
The Chinese University of Hong Kong | CUHK · Department of Mathematics

PhD

About

163
Publications
24,034
Reads
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3,558
Citations
Additional affiliations
August 2008 - August 2015
Hong Kong Baptist University
Position
  • Professor (Assistant)

Publications

Publications (163)
Article
Full-text available
Color image segmentation is a key technology in image processing. In this paper, a two-stage image segmentation method is proposed that is based on the nonconvex \(L_1/L_2\) approximation of the Mumford-Shah (MS) model. Wherein, the nonconvex regularization term \(L_1/L_2\) on the gradient can approximate the Hausdorff measure and extract more boun...
Article
Full-text available
In this paper, we present primal-dual splitting algorithms for the convex minimization problem involving smooth functions with Lipschitzian gradient, finite sum of nonsmooth proximable functions, and linear composite functions. Many total variation-based image processing problems are special cases of such problems. The obtained primal-dual splittin...
Conference Paper
Single-image super-resolution (SISR) has achieved significant breakthroughs with the development of deep learning. However, these methods are difficult to be applied in real-world scenarios since they are inevitably accompanied by the problems of computational and memory costs caused by the complex operations. To solve this issue, we propose a Ligh...
Article
Discovering hidden pattern from imbalanced data is a critical issue in various real-world applications. Existing classification methods usually suffer from the limitation of data especially for minority classes, and result in unstable prediction and low performance. In this paper, a deep generative classifier is proposed to mitigate this issue via...
Article
With the use of convolutional neural networks, Single-Image Super-Resolution (SISR) has advanced dramatically in recent years. However, we notice a phenomenon that the structure of all these models must be consistent during training and testing. This severely limits the flexibility of the model, making the same model difficult to be deployed on dif...
Preprint
Full-text available
Single image denoising (SID) has achieved significant breakthroughs with the development of deep learning. However, the proposed methods are often accompanied by plenty of parameters, which greatly limits their application scenarios. Different from previous works that blindly increase the depth of the network, we explore the degradation mechanism o...
Preprint
Phase retrieval is an important problem with significant physical and industrial applications. In this paper, we consider the case where the magnitude of the measurement of an underlying signal is corrupted by Gaussian noise. We introduce a convex augmentation approach for phase retrieval based on total variation regularization. In contrast to popu...
Article
Color image restoration is one of the basic tasks in pattern recognition. Unlike grayscale image, each color image has three channels in the RGB color space. Due to the inner-relationship within the three channels, color image restoration is usually much more difficult than its grayscale counterpart. Indeed, new problems such as color artifacts cou...
Article
Semantic segmentation has achieved great progress by effectively fusing features of contextual information. In this article, we propose an end-to-end semantic attention boosting (SAB) framework to adaptively fuse the contextual information iteratively across layers with semantic regularization. Specifically, we first propose a pixelwise semantic at...
Article
Recovering an unknown object from the magnitude of its Fourier transform is a phase retrieval problem. Here, we consider a much difficult case, where those observed intensity values are incomplete and contaminated by both salt-and-pepper and random-valued impulse noise. To take advantage of the low-rank property within the image of the object, we u...
Article
High-resolution (HR) remote sensing imagery plays a critical role in remote sensing image interpretation, and single image super-resolution (SISR) reconstruction technology is becoming increasingly valuable and significant. The state-of-the-art deep-learning-based SISR methods have demonstrated remarkable advantages, while reconstructing complex te...
Article
Deep neural networks have achieved great success in medical image segmentation problems such as liver, kidney, the accuracy of which already exceeds human level. However, small organ segmentation (e.g., pancreas) is still a challenging task. To tackle such problems, extracting and aggregating multi-scale robust features become essentially important...
Article
Full-text available
In this paper, we address the minimizing problem of the nonconvex and nonsmooth functions on Hadamard manifolds, and develop an improved proximal gradient method. First, by utilizing the geometric structure of non-positive curvature manifolds, we propose a monotone proximal gradient algorithm with fixed step size on Hadamard manifolds. Then, a conv...
Preprint
Full-text available
Single-image super-resolution (SISR) is an important task in image processing, which aims to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and has achieved promising results with the help of deep learning (DL). In this survey, we give an overview of DL-based SISR methods and group them according to their targets, su...
Chapter
A proper initialization of parameters in a neural network can facilitate its training. The Xavier initialization introduced by Glorot and Bengio which is later generalized to Kaiming initialization by He, Zhang, Ren and Sun are now widely used. However, from experiments we find that networks with heavy weight sharing are difficulty to train even wi...
Preprint
Full-text available
Single image deraining is important for many high-level computer vision tasks since the rain streaks can severely degrade the visibility of images, thereby affecting the recognition and analysis of the image. Recently, many CNN-based methods have been proposed for rain removal. Although these methods can remove part of the rain streaks, it is diffi...
Article
Hyperspectral anomaly detection, which is aimed at locating anomaly, has received widespread attention. In this article, a new anomaly detector, named local spatial constraint and total variation (LSC-TV), is proposed for hyperspectral imagery. In anomaly detection methods based on low-rank representation, background pixels are usually considered t...
Article
Image segmentation is of great importance in image processing. In this paper, we propose a two-stage image segmentation strategy based on the nonconvex ℓ2−ℓp approximation of the Mumford–Shah (MS) model, where we use the nonconvex ℓp (0<p<1) regularizer to approximate the Hausdorff measure and to extract more boundary information. In the first stag...
Article
Convolutional neural networks (CNNs) have been applied to many image processing tasks and achieve great successes. In order to extract common features, every pixel in an image shares the same filters. However, pixels in different regions of an image varies dramatically and shared filters may lose some important local information. Rather than shared...
Article
Junction plays an important role in biomedical research such as retinal biometric identification, retinal image registration, eye-related disease diagnosis and neuron reconstruction. However, junction detection in original biomedical images is extremely challenging. For example, retinal images contain many tiny blood vessels with complicated struct...
Preprint
Full-text available
Scene recovery is a fundamental imaging task for several practical applications, e.g., video surveillance and autonomous vehicles, etc. To improve visual quality under different weather/imaging conditions, we propose a real-time light correction method to recover the degraded scenes in the cases of sandstorms, underwater, and haze. The heart of our...
Conference Paper
Full-text available
Scene recovery is a fundamental imaging task for several practical applications, e.g., video surveillance and autonomous vehicles, etc. To improve visual quality under different weather/imaging conditions, we propose a real-time light correction method to recover the degraded scenes in the cases of sandstorms, underwater, and haze. The heart of our...
Chapter
As a fundamental and challenging task in many subjects such as image processing and computer vision, image segmentation is of great importance but is constantly challenging to deliver, particularly, when the given images or data are corrupted by different types of degradations like noise, information loss, and/or blur. In this article, we introduce...
Article
We present Deep Tensor Canonical Correlation Analysis (DTCCA), a method to learn complex nonlinear transformations of multiple views (more than two) of data such that the resulting representations are linearly correlated in high order. The high-order correlation of given multiple views is modeled by covariance tensor, which is different from most C...
Preprint
Full-text available
There has been an arising trend of adopting deep learning methods to study partial differential equations (PDEs). In this paper, we introduce a deep recurrent framework for solving time-dependent PDEs without generating large scale data sets. We provide a new perspective, that is, a different type of architecture through exploring the possible conn...
Article
Morphology reconstruction of neurons from 3D microscopic images is essential to neuroscience research. However, many reconstructions may contain errors and ambiguities because of the cross-over neuronal fibers. In this paper, an automatic algorithm is proposed for the detection and separation of cross-over structures and is applied to neuron tracin...
Article
Imbalance classification is a challenging research topic in the community of machine learning, in which it is difficult to acquire the discriminative features. To date, a series of methods have been proposed but they still suffer from the following issues. The first issue is caused by the underrepresented data where the boundaries between classes a...
Preprint
Full-text available
In this work, we propose a learning method for solving the linear transport equation under the diffusive scaling. Due to the multiscale nature of our model equation, the model is challenging to solve by using conventional methods. We employ the physical informed neural network (PINN) framework, a mesh-free learning method that can numerically solve...
Article
In this article, we propose a new variational model for segmenting images with intensity inhomogeneity. The proposed model applies simultaneously the local constant and global smoothness priors to describe the bias part such that our model can obtain more precise segmentation results. This is different from the existing models in which either of su...
Article
To improve the image segmentation quality, it is important to adequately describe the local features of targets in images. In this paper, we develop a novel adaptive total variation based two-stage segmentation approach to restore and segment images under complex degradations. To find a smooth approximation solution in the first stage, we introduce...
Article
The digital reconstruction of neurons is essential to various neuroscientific studies. Due to the existence of gaps and ambiguities in neuron images, the neuron tracing results generated by most automatic reconstruction algorithms may be incomplete, resulting in false negatives (FNs), which need to be repaired in proof editing. However, the automat...
Article
Breast ultrasound segmentation is a challenging task in practice due to speckle noise, low contrast and blurry boundaries. Although numerous methods have been developed to solve this problem, most of them can not produce a satisfying result due to uncertainty of the segmented region without specialized domain knowledge. In this paper, we propose a...
Preprint
A spatially fixed parameter of regularization item for whole images doesn't perform well both at edges and smooth areas. A large parameter of regularization item reduces noise better in smooth area but blurs edges, while a small parameter sharpens edges but causes residual noise. In this paper, an automated spatially dependent regularization parame...
Article
Full-text available
The single particle reconstruction (SPR) in cryogenic electron microscopy is considered in this paper. This is an emerging technique for determining the three-dimensional (3D) structure of biological specimens from a limited number of the micrographs. Because the micrographs are modulated by contrast transfer functions and corrupted by heavy noise,...
Preprint
In ptychography experiments, redundant scanning is usually required to guarantee the stable recovery, such that a huge amount of frames are generated, and thus it poses a great demand of parallel computing in order to solve this large-scale inverse problem. In this paper, we propose the overlapping Domain Decomposition Methods (DDMs) to solve the n...
Article
Cauchy noise, as a typical non-Gaussian noise, appears frequently in many important fields, such as radar, medical, and biomedical imaging. In this letter, we focus on image recovery under Cauchy noise. Instead of the celebrated total variation or low-rank prior, we adopt a novel deep-learning-based image denoiser prior to effectively remove Cauchy...
Article
In this paper, we investigate the challenging task of removing haze from a single natural image. The analysis on the haze formation model shows that the atmospheric veil has much less relevance to chrominance than luminance, which motivates us to neglect the haze in the chrominance channel and concentrate on the luminance channel in the dehazing pr...
Preprint
Full-text available
Image demosaicing and denoising are key steps for color image production pipeline. The classical processing sequence consists in applying denoising first, and then demosaicing. However, this sequence leads to oversmoothing and unpleasant checkerboard effect. Moreover, it is very difficult to change this order, because once the image is demosaiced,...
Article
Recovering a signal from its Fourier magnitude is referred to as phase retrieval, which occurs in different fields of engineering and applied physics. This paper gives a new characterization of the phase retrieval problem. Particularly useful is the analysis revealing that the common gradient-based regularization does not restrict the set of soluti...
Article
With the development of machine learning and artificial intelligence, many convolutional neural networks (CNNs) based segmentation methods have been proposed for 3D cardiac segmentation. In this paper, we propose the category attention boosting (CAB) module, which combines the deep network calculation graph with the boosting method. On the one hand...
Article
Image restoration is an important technique to deal with the degradation of the image. This paper presents an efficient and trusty denoising scheme, which combines the convolutional neural network (CNN) technique with the traditional variational model, to offer interpretable and high quality reconstructions. In this scheme, CNN, which has proven ef...
Article
Diffusion‐weighted imaging (DWI) is increasingly used to guide the clinical management of patients with breast tumours. However, accurate tumour characterization with DWI and the corresponding apparent diffusion coefficient (ADC) maps are challenging due to their limited resolution. This study aimed to produce super‐resolution (SR) ADC images and t...
Preprint
Full-text available
We present Deep Tensor Canonical Correlation Analysis (DTCCA), a method to learn complex nonlinear transformations of multiple views (more than two) of data such that the resulting representations are linearly correlated in high order. The high-order correlation of given multiple views is modeled by covariance tensor, which is different from most C...
Article
Imbalanced data classification, as a challenging task, has drawn a significant interest in numerous scientific areas. One popular strategy to balance the instance quantities between two classes is oversampling via generating synthetic instances. However, it still suffers from two key issues: where and how many synthetic instances should be generate...
Article
We present a probabilistic semi-supervised learning (SSL) framework based on sparse graph structure learning. Different from existing SSL methods with either a predefined weighted graph heuristically constructed from the input data or a learned graph based on the locally linear embedding assumption, the proposed SSL model is capable of learning a s...
Preprint
Full-text available
Objective: School reopening has not yet started in China where the COVID-19 outbreak has reached ending stage, largely due to a great concern about COVID-19 infections on students. We attempted to quantitatively evaluate the risk of COVID-19 infections on students caused by school reopening. Study design: We collected the data of the numbers of tea...
Article
Full-text available
The COVID-19 outbreak in China appears to reach the late stage since late March 2020, and a stepwise restoration of economic operations is implemented. Risk assessment for such economic restoration is of significance. Here, we estimated the probability of COVID-19 resurgence caused by work resuming in typical provinces/cities and found that such pr...
Article
Natural images usually are composed of multiple objects at different scales in both flat and slanted regions. Traditional labeling/segmentation approaches based on total variation minimization will produce staircase results, which contain discontinuous regions and unsmoothed boundaries. In this paper, we propose a novel weighted variational model f...
Article
Full-text available
The task of single image super-resolution (SISR) is a highly ill-posed inverse problem since reconstructing the highfrequency details from a low-resolution image is challenging. Most previous CNN-based super-resolution (SR) methods tend to directly learn the mapping from the low-resolution image to the high-resolution image through some complex con...
Article
Full-text available
The restoration of images corrupted by blurring and structured noise has attracted growing attention in the domains of image processing and computer vision. However, many works only focus on the restoration of the images degraded by blurring and additive structured noise or multiplicative structured noise separately. It is still a challenge and an...
Preprint
Full-text available
An ongoing outbreak of atypical pneumonia caused by the 2019 novel coronavirus (SARS-CoV-2) is hitting Wuhan City and has spread to other provinces/cities of China and overseas. It very urgent to forecast the future course of the outbreak. Here, we provide an estimate of the potential total number of confirmed cases in mainland China by applying Bo...