Tianfang Zhang

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I am pursuing a PhD at the Laboratory of Imaging Detection and Intelligent Perecption (IDIP Lab) of University of Electronic Science and Technology of China (UESTC), supresived by Prof. Zhenming Peng

My PhD research is dedicated to developing trustworthy infrared small target detection methods and also extended to sparse representation, optimization, deep learning and deep unfolding networks. In collaboration with my colleagues, my research interests also include segmentation and image compressive sensing.

Previously, I received a Bachelor’s degree from UESTC (2017). And I had the pleasure of working at the Image Section of Department of Computer Science, University of Copenhagen as a visiting PhD, supervised by Prof. Jon Sporring and funded by China Scholarship Council (CSC).

News

08 Nov, 2023	🎉🎉🎉 Our work focus on infrared small target detection via deep unfolding network titled RPCANet was accpeted by IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2024.
03 Oct, 2023	Code and pre-trained model of CT-Net and LR-CSNet are released in Github CT-Net and Github LR-CSNet.
13 Sep, 2023	Our paper focus on Image Compressive Sensing named CT-Net was published online by Knowledge-Based Systems(KBS).

Selected Publications

Find the full list here. Authors marked by * contributed equally.

WACV 2024

RPCANet: Deep Unfolding RPCA Based Infrared Small Target Detection

Fengyi Wu*, Tianfang Zhang*, Lei Li, Yian Huang, and Zhenming Peng

IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

abstract arXiv Code

Deep learning (DL) networks have achieved remarkable performance in infrared small target detection (ISTD). However, these structures exhibit a deficiency in interpretability and are widely regarded as black boxes, as they disregard domain knowledge in ISTD. To alleviate this issue, this work proposes an interpretable deep network for detecting infrared dim targets, dubbed RPCANet. Specifically, our approach formulates the ISTD task as sparse target extraction, low-rank background estimation, and image reconstruction in a relaxed Robust Principle Component Analysis (RPCA) model. By unfolding the iterative optimization updating steps into a deep-learning framework, time-consuming and complex matrix calculations are replaced by theory-guided neural networks. RPCANet detects targets with clear interpretability and preserves the intrinsic image feature, instead of directly transforming the detection task into a matrix decomposition problem. Extensive experiments substantiate the effectiveness of our deep unfolding framework and demonstrate its trustworthy results, surpassing baseline methods in both qualitative and quantitative evaluations.
KBS

Optimization-inspired Cumulative Transmission Network for image compressive sensing

Tianfang Zhang*, Lei Li*, and Zhenming Peng

Knowledge-Based Systems, 2023

abstract HTML Code

Compressive Sensing (CS) techniques enable accurate signal reconstruction with few measurements. Deep Unfolding Networks (DUNs) have recently been shown to increase the efficiency of CS by emulating iterative CS optimization procedures by neural networks. However, most of these DUNs suffer from redundant update procedures or complex matrix operations, which can impair their reconstruction performances. Here we propose the optimization-inspired Cumulative Transmission Network (CT-Net), a DUN approach for natural image CS. We formulate an optimization procedure introducing an auxiliary variable similar to Half Quadratic Splitting (HQS). Unfolding this procedure defines the basic structure of our neural architecture, which is then further refined. A CT-Net is composed of Reconstruction Fidelity Modules (RFMs) for minimizing the reconstruction error and Constraint Gradient Approximation (CGA) modules for approximating (the gradient of) sparsity constraints instead of relying on an analytic solutions such as soft-thresholding. Furthermore, a lightweight Cumulative Transmission (CT) between CGAs in each reconstruction stage is proposed to facilitate a better feature representation. Experiments on several widely used natural image benchmarks illustrate the effectiveness of CT-Net with significant performance improvements and fewer network parameters compared to existing state-of-the-art methods. The experiments also demonstrate the scene and noise robustness of the proposed method.
TAES

Attention-Guided Pyramid Context Networks for Detecting Infrared Small Target Under Complex Background

Tianfang Zhang, Lei Li, Siying Cao, Tian Pu, and Zhenming Peng

IEEE Transactions on Aerospace and Electronic Systems, 2023

abstract HTML Code

Infrared small target detection techniques remain a challenging task due to the complex background. To overcome this problem, by exploring context information, this research presents a data-driven approach called Attention-Guided Pyramid Context Network (AGPCNet). Specifically, we design Attention-Guided Context Block (AGCB) and perceive pixel correlations within and between patches at specific scales via Local Semantic Association (LSA) and Global Context Attention (GCA) respectively. Then the contextual information from multiple scales is fused by Context Pyramid Module (CPM) to achieve better feature representation. In the upsampling stage, we fuse the low and deep semantics through Asymmetric Fusion Module (AFM) to retain more information about small targets. The experimental results illustrate that AGPCNet has achieved state-of-the-art performance on three available infrared small target datasets.
ICCC 2022 Oral

LR-CSNet: Low-rank Deep Unfolding Network for Image Compressive Sensing

Tianfang Zhang, Lei Li, Christian Igel, Stefan Oehmcke, Fabian Gieseke, and Zhenming Peng

In Proceedings of the 8th IEEE International Conference on Computer and Communications, 2022

abstract arXiv HTML Code

Deep unfolding networks (DUNs) have proven to be a viable approach to compressive sensing (CS). In this work, we propose a DUN called low-rank CS network (LR-CSNet) for natural image CS. Real-world image patches are often well-represented by low-rank approximations. LR-CSNet exploits this property by adding a low-rank prior to the CS optimization task. We derive a corresponding iterative optimization procedure using variable splitting, which is then translated to a new DUN architecture. The architecture uses low-rank generation modules (LRGMs), which learn low-rank matrix factorizations, as well as gradient descent and proximal mappings (GDPMs), which are proposed to extract high-frequency features to refine image details. In addition, the deep features generated at each reconstruction stage in the DUN are transferred between stages to boost the performance. Our extensive experiments on three widely considered datasets demonstrate the promising performance of LR-CSNet compared to state-of-the-art methods in natural image CS.
Neurocomputing

Infrared small target detection via self-regularized weighted sparse model

Tianfang Zhang, Zhenming Peng, Hao Wu, Yanmin He, Chaohai Li, and Chunping Yang

Neurocomputing, 2021

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Infrared search and track (IRST) system is widely used in many fields, however, it’s still a challenging task to detect infrared small targets in complex background. This paper proposed a novel detection method called self-regularized weighted sparse (SRWS) model. The algorithm is designed for the hypothesis that data may come from multi-subspaces. And the overlapping edge information (OEI), which can detect the background structure information, is applied to constrain the sparse item and enhance the accuracy. Furthermore, the self-regularization item is applied to mine the potential information in background, and extract clutter from multi-subspaces. Therefore, the infrared small target detection problem is transformed into an optimization problem. By combining the optimization function with alternating direction method of multipliers (ADMM), we explained the solution method of SRWS and optimized its iterative convergence condition. A series of experimental results show that the proposed method outperforms state-of-the-art baselines.
Remote Sens.

Infrared small target detection based on non-convex optimization with Lp-norm constraint

Tianfang Zhang, Hao Wu, Yuhan Liu, Lingbing Peng, Chunping Yang, and Zhenming Peng

Remote Sensing, 2019

abstract HTML Code

The infrared search and track (IRST) system has been widely used, and the field of infrared small target detection has also received much attention. Based on this background, this paper proposes a novel infrared small target detection method based on non-convex optimization with Lp-norm constraint (NOLC). The NOLC method strengthens the sparse item constraint with Lp-norm while appropriately scaling the constraints on low-rank item, so the NP-hard problem is transformed into a non-convex optimization problem. First, the infrared image is converted into a patch image and is secondly solved by the alternating direction method of multipliers (ADMM). In this paper, an efficient solver is given by improving the convergence strategy. The experiment shows that NOLC can accurately detect the target and greatly suppress the background, and the advantages of the NOLC method in detection efficiency and computational efficiency are verified.