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Generative artificial Intelligence for Hyperspectral Sensor Data: A Review

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 6422-6439页

作者： Abuhani, Diaa Addeen Zualkernan, Imran Aldamani, Raghad Alshafai, Mohamed Amer Univ Sharjah Dept Comp Sci & Engn Sharjah 26666 U Arab Emirates

Airborne platforms and satellites provide rich sensor data in the form of hyperspectral images (HSI), which are crucial for numerous vision-related tasks, such as feature extraction, image enhancement, and data synthesis. This article reviews the contextual importance and applications of generative artificial intelligence (GAI) in the advancement of HSI processing. GAI methods address the inherent challenges of HSI data, such as high dimensionality, noise, and the need to preserve spectral-spatial correlations, rendering them indispensable for modern HSI analysis. Generative neural networks, including generative adversarial networks and denoising diffusion probabilistic models, are highlighted for their superior performance in classification, segmentation, and object identification tasks, often surpassing traditional approaches, such as U-Nets, autoencoders, and deep convolutional neural networks. Diffusion models showed competitive performance in tasks, such as feature extraction and image resolution enhancement, particularly in terms of inference time and computational cost. Transformer architectures combined with attention mechanisms further improved the accuracy of generative methods, particularly for preserving spectral and spatial information in tasks, such as image translation, data augmentation, and data synthesis. Despite these advancements, challenges remain, particularly in developing computationally efficient models for super-resolution and data synthesis. In addition, novel evaluation metrics tailored to the complex nature of HSI data are needed. This review underscores the potential of GAI in addressing these challenges while presenting its current strengths, limitations, and future research directions.

关键词： Feature extraction Hyperspectral imaging image segmentation Satellites Spatial resolution Machine learning neural networks Reviews Noise measurement Data models Diffusion models generative adversarial networks (GANs) generative artificial intelligence (GAI) generative neural networks (GNNs) hyperspectral images

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Application of deep reinforcement learning in various image processing tasks: a survey

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EVOLVING SYSTEMS 2025年第1期16卷 1-20页

作者： Tadesse, Daniel Moges Kebede, Samuel Rahimeto Debele, Taye Girma Waldamichae, Fraol Gelana Ethiopian Artificial Intelligence Inst Addis Ababa 40782 Ethiopia Addis Ababa Sci & Technol Univ Coll Elect & Mech Engn Addis Ababa 120611 Ethiopia Debreberhan Univ Coll Engn Debreberhan 222 Ethiopia

A subset of machine learning algorithm called Deep Reinforcement Learning (DRL) enables computers or agents to learn behavior by taking actions in a given environment through trial and error while observing the rewards. In this learning paradigm, the agent is given a set of actions to chose and is then rewarded or punished depending on the results of those actions. The agent gradually develops the ability to make the best decisions by maximizing its rewards. DRL blends the learning ability of deep neural networks into the decision making capability of reinforcement learning (RL) frameworks in order to seeks and identify the most favorable set of actions. This survey paper studies DRL applications for diverse image processing tasks. It starts by providing an overview of the latest model-free and model-based RL and DRL algorithms. Then, it looks at how DRL is being used for various image processing tasks including image segmentation and classification, object detection, image registration, image denoising, image restoration, and landmark detection. Lastly, the paper discusses the potential uses and challenges of DRL in the proposed area by addressing the research questions. Survey results have showed that DRL is a promising approach for image processing and that it has the potential to solve complex image processing tasks.

关键词： Deep reinforcement learning Deep neural networks image processing

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A review of hyperspectral image classification based on graph neural networks

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artificial INTELLIGENCE REVIEW 2025年第6期58卷 1-56页

作者： Zhao, Xiaofeng Ma, Junyi Wang, Lei Zhang, Zhili Ding, Yao Xiao, Xiongwu Xian Res Inst High Technol Key Lab Opt Engn Xian 710025 Shaanxi Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430079 Peoples R China

Hyperspectral images provide rich spectral-spatial information but pose significant classification challenges due to high dimensionality, noise, mixed pixels, and limited labeled samples. Graph neural networks (GNNs) have emerged as a promising solution, offering a semi-supervised framework that can capture complex spatial-spectral relationships inherent in non-Euclidean hyperspectral image data. However, existing reviews often concentrate on specific aspects, thus limiting a comprehensive understanding of GNN-based hyperspectral image classification. This review systematically outlines the fundamental concepts of hyperspectral image classification and GNNs, and summarizes leading approaches from both traditional machine learning and deep learning. Then, it categorizes GNN-based methods into four paradigms: graph recurrent neural networks, graph convolutional networks, graph autoencoders, and hybrid graph neural networks, discussing their theoretical underpinnings, architectures, and representative applications. Finally, five key directions are further highlighted: adaptive graph construction, dynamic graph processing, deeper architectures, self-supervised strategies, and robustness enhancement. These insights aim to facilitate continued innovation in GNN-based hyperspectral imaging, guiding researchers toward more efficient and accurate classification frameworks.

关键词： Graph neural Hyperspectral image classification Deep learning

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Bio-Inspired Neuromorphic Sensory Systems from Intelligent Perception to Nervetronics

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ADVANCED SCIENCE 2025年第1期12卷 e2409568页

作者： Boahen, Elvis K. Kweon, Hyukmin Oh, Hayoung Kim, Ji Hong Lim, Hayoung Kim, Do Hwan Hanyang Univ Dept Chem Engn Seoul 04763 South Korea Hanyang Univ Inst Nano Sci & Technol Seoul 04763 South Korea Hanyang Univ Clean Energy Res Inst Seoul 04763 South Korea Stanford Univ Dept Chem Engn Stanford CA 94305 USA

Inspired by the extensive signal processing capabilities of the human nervous system, neuromorphic artificial sensory systems have emerged as a pivotal technology in advancing brain-like computing for applications in humanoid robotics, prosthetics, and wearable technologies. These systems mimic the functionalities of the central and peripheral nervous systems through the integration of sensory synaptic devices and neural network algorithms, enabling external stimuli to be converted into actionable electrical signals. This review delves into the intricate relationship between synaptic device technologies and neural network processing algorithms, highlighting their mutual influence on artificial intelligence capabilities. This study explores the latest advancements in artificial synaptic properties triggered by various stimuli, including optical, auditory, mechanical, and chemical inputs, and their subsequent processing through artificial neural networks for applications in image recognition and multimodal pattern recognition. The discussion extends to the emulation of biological perception via artificial synapses and concludes with future perspectives and challenges in neuromorphic system development, emphasizing the need for a deeper understanding of neural network processing to innovate and refine these complex systems.

关键词： artificial synapse bio-inspired machine learning neuromorphic sensory system

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Efficient Implementation of Matrix-Based image processing Algorithms for IoT applications

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APPLIED SCIENCES-BASEL 2025年第9期15卷 4947-4947页

作者： Zoican, Sorin Zoican, Roxana POLITEHN Bucharest Natl Univ Sci & Technol Fac Elect Telecommun & Informat Technol Telecommun Dept Bucharest 060042 Romania

This paper analyzes implementation approaches of matrix-based image processing algorithms. As an example, an image processing algorithm that provides both image compression and image denoising using random sample consensus and discrete cosine transform is analyzed. Two implementations are illustrated: one using the Blackfin processor with 32-bit fixed-point representation and the second using the convolutional neural network (CNN) accelerator in the MAX78000 microcontroller. Implementation with Blackfin can be considered a classic approach, in C language, possible on all existing microcontrollers. This implementation is improved by using two cores. The proposed implementation with the CNN accelerator is a new approach that effectively uses the dedicated accelerator for convolutional neural networks, with better results than a classical implementation. The execution time of matrix-based image processing algorithms can be reduced by using CNN accelerators already integrated in some modern microcontrollers to implement artificial intelligence algorithms. The proposed method uses CNN in a different way, not for artificial intelligence algorithms, but for matrix calculations using CNN resources effectively while maintaining the accuracy of the calculations. A comparison of these two implementations and the validation using MATLAB with 64 bits floating point representation are conducted. The obtained performance is good both in terms of quality of reconstructed image and execution time, and the performance differences between the infinite precision implementation and the finite precision implementation are small. The CNN accelerator implementation, based on matrix multiplication implemented using CNN, has a better performance suitable for Internet of Things applications.

关键词： discrete cosine transform random sample consensus visual DSP plus plus blackfin processor CNN accelerator internet of things

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A Nonlinear Noise-Resistant Zeroing neural Network Model for Solving Time-Varying Quaternion Generalized Lyapunov Equation and applications to Color image processing

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IEEE TRANSACTIONS ON neural networks AND LEARNING SYSTEMS 2025年 PP卷 PP页

作者： Xiao, Lin Yan, Xiangru He, Yongjun Luo, Biao Song, Qiya Hunan Normal Univ Hunan Prov Key Lab Intelligent Comp & Language Inf Changsha 410081 Peoples R China Hunan Normal Univ MOE LCSM Changsha 410081 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China

The time-varying Lyapunov equation (TVLE) plays a crucial role in control design and system stability. However, there has been limited research conducted on the time-varying generalized Lyapunov equation in the quaternion field. To tackle the time-varying quaternion generalized Lyapunov equation, a nonlinear noise-resistant zeroing neural network (NNR-ZNN) model with a novel power activation function (NPAF) is devised. The issue of non-commutativity within quaternion is circumvented by utilizing the real representation. The theoretical analyses provide a sufficient explanation for the global stability, fixed-time convergence, and robustness of the NNR-ZNN model. Under several different kinds of noises, the exceptional robustness of the NNR-ZNN model is highlighted by comparison with other existing models. In the end, the successful applications of the NNR-ZNN model to color image fusion and color image denoising confirm the practical value of the NNR-ZNN model.

关键词： Mathematical models Quaternions Color artificial neural networks Numerical models Noise Noise reduction Convergence Robustness Stability analysis Activation function (AF) color image denoising color image fusion noise resistance time-varying quaternion generalized Lyapunov equation (TVQGLE) zeroing neural network (ZNN)

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Accelerating Convolutional processing by Harnessing Channel Shifts in Arrayed Waveguide Gratings

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LASER & PHOTONICS REVIEWS 2025年第1期19卷

作者： Yi, Dan Zhao, Caiyue Zhang, Zunyue Xu, Hongnan Tsang, Hon Ki Chinese Univ Hong Kong Dept Elect Engn Shatin Hong Kong Peoples R China Tianjin Univ Sch Precis Instrument & Optoelect Engn Tianjin 300072 Peoples R China

Convolutional neural networks are a powerful category of artificial neural networks that can extract features from raw data to provide greatly reduced parametric complexity and enhance pattern recognition and the accuracy of prediction. Optical neural networks offer the promise of dramatically accelerating computing speed while maintaining low power consumption even when using high-speed data streams running at hundreds of gigabit/s. Here, we propose an optical convolutional processor (CP) that leverages the spectral response of an arrayed waveguide grating (AWG) to enhance convolution speed by eliminating the need for repetitive element-wise multiplication. Our design features a balanced AWG configuration, enabling both positive and negative weightings essential for convolutional kernels. A proof-of-concept demonstration of an 8-bit resolution processor is experimentally implemented using a pair of AWGs with a broadband Mach-Zehnder interferometer (MZI) designed to achieve uniform weighting across the whole spectrum. Experimental results demonstrate the CP's effectiveness in edge detection and achieved 96% accuracy in a convolutional neural network for MNIST recognition. This approach can be extended to other common operations, such as pooling and deconvolution in Generative Adversarial networks. It is also scalable to more complex networks, making it suitable for applications like autonomous vehicles and real-time video recognition. A novel convolutional processor is proposed using the shifted spectral response of a pair of arrayed waveguide gratings (AWGs) to mimic the kernel shifts during image convolution. This inherent mixing of inputs in the AWG's spectral response eliminates the need for repetitive element-wise computations while enabling the simultaneous generation of convolved output maps. image

关键词： array waveguide grating convolutional processor deep learning image processing optical computing

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DiffStyler: Controllable Dual Diffusion for Text-Driven image Stylization

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IEEE TRANSACTIONS ON neural networks AND LEARNING SYSTEMS 2025年第2期36卷 3370-3383页

作者： Huang, Nisha Zhang, Yuxin Tang, Fan Ma, Chongyang Huang, Haibin Dong, Weiming Xu, Changsheng Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Kuaishou Technol Beijing 100085 Peoples R China

Despite the impressive results of arbitrary image-guided style transfer methods, text-driven image stylization has recently been proposed for transferring a natural image into a stylized one according to textual descriptions of the target style provided by the user. Unlike the previous image-to-image transfer approaches, text-guided stylization progress provides users with a more precise and intuitive way to express the desired style. However, the huge discrepancy between cross-modal inputs/outputs makes it challenging to conduct text-driven image stylization in a typical feed-forward CNN pipeline. In this article, we present DiffStyler, a dual diffusion processing architecture to control the balance between the content and style of the diffused results. The cross-modal style information can be easily integrated as guidance during the diffusion process step-by-step. Furthermore, we propose a content image-based learnable noise on which the reverse denoising process is based, enabling the stylization results to better preserve the structure information of the content image. We validate the proposed DiffStyler beyond the baseline methods through extensive qualitative and quantitative experiments. The code is available at https://***/haha-lisa/Diffstyler.

关键词： Arbitrary image stylization diffusion textual guidance neural network applications

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A hardware accelerator to support deep learning processor units in real-time image processing

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ENGINEERING applications OF artificial INTELLIGENCE 2025年 145卷

作者： Cittadini, Edoardo Marinoni, Mauro Buttazzo, Giorgio Scuola Super Sant Anna Pisa Italy

Deep neural networks are becoming crucial in many cyber-physical systems involving complex perceptual tasks. For those embedded systems requiring real-time interactions with dynamic environments, as autonomous robots and drones, it is of paramount importance that such algorithms are efficiently executed onboard on properly designed hardware accelerators to meet the required performance specifications. In particular, some neural network architectures for object detection and tracking, as You Only Look Once (YOLO), include heavy computational stages that need to be executed before and after the model inference. Such stages are typically not incorporated in traditional accelerators and are executed on general-purpose processors, thus introducing a bottleneck in the overall processing pipeline. To overcome such a problem, this paper presents a generalpurpose accelerator on a field-programmable gate array (FPGA) able to run pre-processing and post-processing operations typically required by vision tasks. The proposed solution has been tested in combination with a YOLO object detector accelerated on an Advanced Micro Devices (AMD) Xilinx Kria KR260 board mounting an UltraScale+ multiprocessor system-on-chip, achieving a significant improvement in terms of both timing performance and power consumption, and enabling onboard visual processing into drones. The proposed solution is able to boost the traditional object detection process by a factor of 4.4, allowing the execution of the full processing pipeline at 60 frames per second (fps), versus 13.6 fps reachable without the proposed accelerator. Asa result, this work enables the use of high-speed cameras for developing more reactive systems that can respond to incoming events with lower latency.

关键词： Hardware acceleration Object detection Deep neural networks Real-time processing image pre-processing acceleration Deep processing unit

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Robust pattern retrieval in an optical Hopfield neural network

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OPTICS LETTERS 2025年第1期50卷 225-228页

作者： Katidis, Michael Musa, Khalid Kumar, Santosh Li, Zhaotong Long, Frederick Qu, Chunlei Huang, Yu-ping Stevens Inst Technol Dept Phys Hoboken NJ 07030 USA Stevens Inst Technol Ctr Quantum Sci & Engn Hoboken NJ 07030 USA US Army DEVCOM Armaments Ctr Picatinny Arsenal NJ 07806 USA

Hopfield neural networks (HNNs) promise broad applications in areas such as combinatorial optimization, memory storage, and pattern recognition. Among various implementations, optical HNNs are particularly interesting because they can take advantage of fast optical matrix-vector multiplications. Yet their studies so far have mostly been on the theoretical side, and the effects of optical imperfections and robustness against memory errors remain to be quantified. Here we demonstrate an optical HNN in a simple experimental setup using a spatial light modulator with 100 neurons. It successfully stores and retrieves 13 patterns, which approaches the critical capacity limit of alpha c = 0.138. It is robust against random phase flipping errors of the stored patterns, achieving high fidelity in recognizing and storing patterns even when 30% pixels are randomly flipped. Our results highlight the potential of optical HNNs in practical applications such as real-time image processing for autonomous driving, enhanced AI with fast memory retrieval, and other scenarios requiring efficient data processing. (c) 2024 Optica Publishing Group. All rights, including for text and data mining (TDM), artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： image processing neural networks Optical neural systems Pattern recognition Real time imaging Spatial light modulators

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