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Efficient and Model-Based Infrared and Visible Image Fusion via algorithm unrolling

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第3期32卷 1186-1196页

作者： Zhao, Zixiang Xu, Shuang Zhang, Jiangshe Liang, Chengyang Zhang, Chunxia Liu, Junmin Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China

Infrared and visible image fusion (IVIF) expects to obtain images that retain thermal radiation information from infrared images and texture details from visible images. In this paper, a model-based convolutional neural network (CNN) model, referred to as algorithm unrolling Image Fusion (AUIF), is proposed to overcome the shortcomings of traditional CNN-based IVIF models. The proposed AUIF model starts with the iterative formulas of two traditional optimization models, which are established to accomplish two-scale decomposition, i.e., separating low-frequency base information and high-frequency detail information from source images. Then the algorithm unrolling is implemented where each iteration is mapped to a CNN layer and each optimization model is transformed into a trainable neural network. Compared with the general network architectures, the proposed framework combines the model-based prior information and is designed more reasonably. After the unrolling operation, our model contains two decomposers (encoders) and an additional reconstructor (decoder). In the training phase, this network is trained to reconstruct the input image. While in the test phase, the base (or detail) decomposed feature maps of infrared/visible images are merged respectively by an extra fusion layer, and then the decoder outputs the fusion image. Qualitative and quantitative comparisons demonstrate the superiority of our model, which can robustly generate fusion images containing highlight targets and legible details, exceeding the state-of-the-art methods. Furthermore, our network has fewer weights and faster speed.

关键词： Optimization Image fusion Feature extraction Task analysis Kernel Decoding Neural networks Image fusion two-scale decomposition algorithm unrolling model-based network structure

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Efficient and Interpretable Deep Blind Image Deblurring Via algorithm unrolling

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2020年 6卷 666-681页

作者： Li, Yuelong Tofighi, Mohammad Geng, Junyi Monga, Vishal Eldar, Yonina C. Penn State Univ Dept Elect Engn University Pk PA 16802 USA Penn State Univ Dept Aerosp Engn University Pk PA 16802 USA Weizmann Inst Sci Fac Math & Comp Sci Dept Elect Engn IL-76100 Rehovot Israel

Blind image deblurring remains a topic of enduring interest. Learning based approaches, especially those that employ neural networks have emerged to complement traditional model based methods and in many cases achieve vastly enhanced performance. That said, neural network approaches are generally empirically designed and the underlying structures are difficult to interpret. In recent years, a promising technique called algorithm unrolling has been developed that has helped connect iterative algorithms such as those for sparse coding to neural network architectures. In this article, we propose a neural network architecture based on this idea. We first present an iterative algorithm that may be considered as a generalization of the traditional total-variation regularization method in the gradient domain. We then unroll the algorithm to construct a neural network for image deblurring which we refer to as Deep unrolling for Blind Deblurring (DUBLID). Key algorithm parameters are learned with the help of training images. Our proposed deep network DUBLID achieves significant practical performance gains while enjoying interpretability and efficiency at the same time. Extensive experimental results show that DUBLID outperforms many state-of-the-art methods and in addition is computationally faster.

关键词： Image deblurring deconvolution deep learning algorithm unrolling interpretable deep networks

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Adaptive Low-Light Image Enhancement Optimization Framework with algorithm unrolling 6th

Adaptive Low-Light Image Enhancement Optimization Framework ...

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6th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： He, Qichang Liang, Lingyu Xiao, Wocheng Liang, Mingju South China Univ Technol Guangzhou Peoples R China Guangdong Artificial Intelligence & Digital Econ Pazhou Lab Guangzhou Guangzhou Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Minist Educ Nanjing Peoples R China Guangdong Foshan Lianchuang Grad Sch Engn Foshan Peoples R China

ISBN: (纸本)9789819985517;9789819985524

Images captured in a dark environment may cause low visibility and lose significant details leading to poor performance of vision-based recognition systems. Recently, deep learning-based methods have been proposed for low-light image enhancement (LIE) with different priors or training schemes. However, even those LIE methods may introduce visual artifacts into the enhanced images. This paper proposes an adaptive LIE optimization framework that allows to re-optimize different deep learning-based LIE methods based on an adaptive quality evaluation (QE). Specifically, we design an interpretable and learnable LIE-QE module for LIE. To find the optimal structure of the LIE-QE module, we propose an algorithm unrolling method to design the LIE-QE module, where the each layer of the decomposition component of the LIE-QE module can be interpreted as WLS edge-aware smoothing. Both qualitative and quantitative experiments were conducted, and the evaluation verified the effectiveness of the proposed learnable deep unrolled LIE-QE module for LIE. The results shows that the proposed LIE framework can effectively improve different deep learning-based LIE methods indicating the potential of the optimization framework with LIE-QE module to re-optimize existing DNN-based LIE methods.

关键词： Low-light Image Enhancement algorithm unrolling

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PHOTON-LIMITED DEBLURRING USING algorithm unrolling 47

PHOTON-LIMITED DEBLURRING USING ALGORITHM UNROLLING

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Sanghvi, Yash Gnanasambandan, Abhiram Chan, Stanley H. Purdue Univ Sch Elect & Comp Engn 465 Northwestern Ave W Lafayette IN 47907 USA

ISBN: (纸本)9781665405409

Image deblurring in a photon-limited condition is ubiquitous in a variety of low-light applications such as photography, microscopy and astronomy. However, the presence of photon shot noise due to a low-illumination and/or short exposure makes the deblurring task substantially more challenging than conventional deblurring. In this paper we present an algorithm unrolling approach that unrolls a Plug-and-Play algorithm using a fixed-iteration network. By changing the conventional two-variable splitting formulation of Plug-and-Play to an alternate three-variable splitting, we obtain a differentiable and end-to-end trainable network. Our algorithm outperforms existing methods for 1dB across different illuminations. We also overcome the difficulty of acquiring real motion blur kernels at low-light by presenting a photon-limited motion deblurring image dataset.

关键词： Poisson deconvolution non-blind deblurring photon-limited imaging algorithm unrolling

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Neural Sum Rate Maximization for AI-Native Wireless Networks: Alternating Direction Method of Multipliers Framework and algorithm unrolling 2

Neural Sum Rate Maximization for AI-Native Wireless Networks...

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2nd International Workshop on Networked AI Systems (NetAISys)

作者： Chen, Siya Tan, Chee Wei City Univ Hong Kong Hong Kong Peoples R China Nanyang Technol Univ Nanyang Ave Singapore Singapore

ISBN: (纸本)9798400706615

In this paper, we introduce Neural Sum Rate Maximization to address nonconvex problems in maximizing sum rates with a total power constraint for downlink multiple access. We combine the optimization-theoretic methods and neural network-based algorithm unrolling techniques to map iterations onto trainable neural network layers, enabling development and deployment in the AI-native layer for future wireless networks. Our approach leverages mathematical structures in sum rate optimization, such as the alternating direction method of multipliers and the standard interference function framework for iterative algorithm design. By integrating algorithm unrolling techniques, our approach can learn from data and significantly enhance efficiency. Numerical experiments demonstrate the method's advantages in efficiency, performance, and interpretability, which are particularly beneficial for resource-constrained optimization in AI-native wireless networks.

关键词： Sum rate maximization Alternating direction method of multipliers Majorization-minimization algorithm unrolling

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Non-Uniform Blind Image Deblurring Using an algorithm unrolling Neural Network 14

Non-Uniform Blind Image Deblurring Using an Algorithm Unroll...

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14th IEEE Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)

作者： Richmond, Greig Cole-Rhodes, Arlene Morgan State Univ Baltimore MD 21239 USA

ISBN: (纸本)9781665478229

In this work we develop a compact neural network that is designed to deblur images that have been affected by a non-uniform blur. We develop this network by unrolling a traditional iterative image deblurring algorithm and adapt it to independently deblur regions of an image. The network is evaluated by comparing its deblurring capabilities with that of other state-of-the-art networks, such as the SRN-Deblur network [13] and the DUBLID network [4]. We investigate the effect that varying the patch size and the size of the point spread function has on the deblurring performance of our network. We evaluate each deblurring network using the industry standard GOPRO [12] and Kohler [25] datasets.

关键词： algorithm unrolling Convolutional neural network Image deblurring Image restoration Non-uniform blur

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Interpretable Neural Network via algorithm unrolling for Mechanical Fault Diagnosis

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1页

作者： An, Botao Wang, Shibin Zhao, Zhibin Qin, Fuhua Yan, Ruqiang Chen, Xuefeng Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China

Artificial neural network (ANN) has achieved great success in mechanical fault diagnosis and has been widely used. However, traditional ANN is still opaque in terms of interpretability, making it difficult for users to understand and trust the diagnosis results. This article proposes an interpretable neural network to provide high-performance and credible mechanical fault diagnosis results. The proposed network is mainly generated by unrolling the nested iterative soft thresholding algorithm (NISTA) for a sparse coding model and it is named NISTA-Net. Therefore, the network architecture of NISTA-Net has a clear theoretical basis, and users know how it is designed. Additionally, we propose a visualization method for NISTA-Net to examine whether the network has learned meaningful features. This method helps users better understand how NISTA-Net performs classifications. These two aspects of transparency/interpretability allow NISTA-Net to be more credible when applied for mechanical fault diagnosis. We carried out simulations and two experiments of fault diagnosis to verify the performance of NISTA-Net. The results reveal that NISTA-Net can well extract the fault features of the concerned bearings and gears. As a consequence, it achieves the best performance compared with other advanced networks. Given the success of NISTA-Net, a systematic approach is finally summarized to help design interpretable fault diagnosis networks, aiming to inspire more related research.

关键词： Feature extraction Fault diagnosis Encoding Vibrations Convolutional codes Classification algorithms Ad hoc networks algorithm unrolling interpretable neural network mechanical fault diagnosis prognostic and health management (PHM) sparse coding

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Multi-frequency progressive refinement for learned inverse scattering

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JOURNAL OF COMPUTATIONAL PHYSICS 2025年 527卷

作者： Melia, Owen Tsang, Olivia Charisopoulos, Vasileios Khoo, Yuehaw Hoskins, Jeremy Willett, Rebecca Univ Chicago Dept Comp Sci Chicago IL 60637 USA Univ Chicago Data Sci Inst Chicago IL 60637 USA Univ Chicago Dept Stat Computat & Appl Math Chicago IL 60637 USA

Interpreting scattered acoustic and electromagnetic wave patterns is a computational task that enables remote imaging in a number of important applications, including medical imaging, geophysical exploration, sonar and radar detection, and nondestructive testing of materials. However, accurately and stably recovering an inhomogeneous medium from far-field scattered wave measurements is a computationally difficult problem, due to the nonlinear and non-local nature of the forward scattering process. We design a neural network, called Multi-Frequency Inverse Scattering Network (MFISNet), and a training method to approximate the inverse map from far-field scattered wave measurements at multiple frequencies. We consider three variants of MFISNet, with the strongest performing variant inspired by the recursive linearization method - a commonly used technique for stably inverting scattered wavefield data - that progressively refines the estimate with higher frequency content. MFISNet outperforms past methods in regimes with high-contrast, heterogeneous large objects, and inhomogeneous unknown backgrounds.

关键词： Machine learning Inverse scattering algorithm unrolling Deep learning Inverse problems

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Self-supervised Scalable Deep Compressed Sensing

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2025年第2期133卷 688-723页

作者： Chen, Bin Zhang, Xuanyu Liu, Shuai Zhang, Yongbing Zhang, Jian Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Harbin Inst Technol Shenzhen Shenzhen Peoples R China

Compressed sensing (CS) is a promising tool for reducing sampling costs. Current deep neural network (NN)-based CS approaches face the challenges of collecting labeled measurement-ground truth (GT) data and generalizing to real applications. This paper proposes a novel Self-supervised sCalable deep CS method, comprising a deep Learning scheme called SCL and a family of Networks named SCNet, which does not require GT and can handle arbitrary sampling ratios and matrices once trained on a partial measurement set. Our SCL contains a dual-domain loss and a four-stage recovery strategy. The former encourages a cross-consistency on two measurement parts and a sampling-reconstruction cycle-consistency regarding arbitrary ratios and matrices to maximize data utilization. The latter can progressively leverage the common signal prior in external measurements and internal characteristics of test samples and learned NNs to improve accuracy. SCNet combines both the explicit guidance from optimization algorithms and the implicit regularization from advanced NN blocks to learn a collaborative signal representation. Our theoretical analyses and experiments on simulated and real captured data, covering 1-/2-/3-D natural and scientific signals, demonstrate the effectiveness, superior performance, flexibility, and generalization ability of our method over existing self-supervised methods and its significant potential in competing against many state-of-the-art supervised methods. Code is available at https://***/Guaishou74851/SCNet.

关键词： Compressed sensing Inverse imaging problems Self-supervised learning algorithm unrolling

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A model-based deep learning approach to interpretable impact force localization and reconstruction

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2025年 224卷

作者： Zhou, Rui Qiao, Baijie Jiang, Liangliang Cheng, Wei Yang, Xiuyue Wang, Yanan Chen, Xuefeng Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Natl Key Lab Aerosp Power Syst & Plasma Technol Xian 710049 Peoples R China Beijing Inst Astronaut Syst Engn Beijing 10076 Peoples R China

Model-based and deep learning-based methods have been widely studied for force identification. However, model-based methods usually have high computational complexity and face challenges in parameter setting, while the generic network structures in deep learning methods are mostly designed empirically. In this paper, a model-based deep learning network is proposed to simultaneously localize and reconstruct impact f[orces. The proposed network, dubbed NSC-Net, is obtained by unrolling the iterative shrinkage-thresholding algorithm (ISTA) for the non- negative sparse coding (NSC) model. NSC-Net combines the interpretability of the model-based ISTA with the powerful parameter learning capability of deep learning. The transfer matrix is embedded into the network as physical information through learnable scaling. The structure of NSC-Net is based on explicit theoretical analysis, which enhances its interpretability in terms of structural design. In contrast to ISTA, the parameters in NSC-Net can be trained end-to-end from the training data without manual setting. Simulations and experiments on composite panels are conducted to validate the performance of the proposed method. The results demonstrate that NSC-Net accurately localizes and reconstructs impact forces, outperforming both ISTA and learned iterative shrinkage-thresholding algorithm (LISTA) network. Additionally, NSC-Net exhibits excellent noise immunity. Furthermore, the systematic approach of constructing an algorithm unrolling network for impact force identification is summarized, aiming to facilitate further related research.

关键词： Impact force identification algorithm unrolling Interpretable deep learning Composite structures Structural health monitoring

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