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6th International Conference on Information Technology and Nanotechnology (IEEE ITNT)

作者： Medvedeva, Kseniya Fursov, Vladimir Samara Natl Res Univ Samara Russia Samara Natl Res Univ Image Proc Syst Inst RAS Branch FSRC Crystallog & Photon Samara Russia

ISBN: (纸本)9781728170411

The article discusses the technology of correcting blurred type distortions on images recorded by mobile devices. The aim of the development is to provide high-quality distortion correction with minimal computational costs. We use two-stage technology. At the first stage, blind identification of linear filter parameters is performed. It is assumed that the frequency response of the filter has central symmetry and consists of segments of quadratic and exponential functions. At the second stage, parameters of nonlinear filter are determined. Both automatic and manual visual settings are provided. The program code for Android OS and the results of experiments showing the effectiveness of the developed application to eliminate distortions in images are presented.

关键词： image processing mobile application linear image processing non-linear image processing

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High-resolution and ultra-low power nonlinear image processing with passive high-quality factor metasurfaces

arXiv

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arXiv 2025年

作者： Zhao, Bo Lin, Lin Samuel, Ameyaw Lawrence, Mark Department of Electrical & Systems Engineering Washington University in St. Louis St. LouisMO63130 United States Department of Chemistry Washington University in St. Louis St. LouisMO63130 United States

image processing is both one of the most exciting domains for applying artificial intelligence and the most computationally expensive. Nanostructured metasurfaces have opened the door to the ultimate energy saving by directly processing ambient image data via ultra-thin layers before detection. However, a key ingredient of universal computation - nonlinear thresholding functions - have yet to be demonstrated for low intensities without an external power source. Here, we present a passive, all-optical method for nonlinear image processing using Silicon nanoantenna arrays. We experimentally demonstrate an intensity thresholding filter capable of processing one-dimensional images with only Watt-level power. By leveraging the opto-thermal Kerr nonlinearity through high-Q guided mode resonance, we achieve an experimental threshold as low as 0.1 mW/μm2 with a spatial resolution of 1.85 μm. Additional simulations indicate that the threshold can be further reduced while maintaining high spatial selectivity. Analog, pixel-wise, smoothed leaky ReLU activation filters promise to revolutionize image sensing. © 2025, CC BY-NC-ND.

关键词： Photointerpretation

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Mobile application for two-stage linear and nonlinear image processing

Mobile application for two-stage linear and nonlinear image ...

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Information Technology and Nanotechnology (ITNT), International Conference on

作者： Kseniya Medvedeva Vladimir Fursov Samara National Research University Samara Russia Image Processing Systems Institute оf RAS – Branch of the FSRC ‘Crystallography and Photonics’ RAS Samara National Research University Samara Russia

ISBN: (数字)9781728170411

ISBN: (纸本)9781728170428

关键词： Training Maximum likelihood detection Visualization nonlinear distortion nonlinear filters Mobile applications Visual perception

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Modeling nonlinear image processing algorithms using a processor based on the sorting node 4

Modeling nonlinear image processing algorithms using a proce...

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Conference on Emerging Imaging and Sensing Technologies for Security and Defence IV part of SPIE Security + Defence Symposium

作者： Krasilenko, Vladimir G. Lazarev, Alexander A. Nikitovich, Diana V. Vinnitsa Social Econ Inst 86-131 Kelestskaya Str UA-21021 Vinnitsa Ukraine Vinnitsa Natl Tech Univ Vinnytsia Ukraine

ISBN: (数字)9781510630307

ISBN: (纸本)9781510630307;9781510630291

In this paper we proposed a new iterative process of sorting an array of signals, which differs from the known structures of sorting signals by uniformity, versatility, which allows direct and inverse sorting of an array of analog or digital signals. We proposed the structure of the processor based on the node that sorts the array of processed signals. Let us show the variety of the sorting node, which can be executed both iterative and pipeline-type, implementation of homogeneous sorting structure, consisting of two layers of base cells and a multichannel sampling and holding device and show that for a large number of operations and functions performed on image processing and filtering, it is necessary to sort by the signal level in the selected image window. The base cells consist of no more than 20 CMOS 1.5 mu m transistors, the total power consumption of the sorting node on 10 continuously logical base cells ( CL BC) is 2mW, the supply voltage is 1.8 divided by 3.3V, the range of an input photocurrent is 0.1 divided by 24 mu A, the conversion cycle is 10 mu s. The paper considers results of design and modeling of CL BC based on current mirrors (CM) for creating picture type image processors (IP) with matrix parallel inputs-outputs. Such sorting nodes based on them have a number of advantages: high speed and reliability, simplicity, small power consumption, high integration level. The inclusion of an iterative node for sorting signals into a modified nonlinear IP structure makes it possible to significantly simplify its design and increase the functional capabilities of such processor. We evaluated the technical parameters of such a relational preprocessor. The simulation results confirm the proposed approaches to the design of sorting nodes of analog signals of the iterative type, which simplify the complexity of the nodes by an order of magnitude, ensuring their uniformity, regularity and simplicity of scaling. The power consumption of the processors does not e

关键词： analog relational preprocessor image nonlinear processing simulation continuous logic current mirror sorting networks methods of selection and rank preprocessing rank filtering

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Analog image processing with nonlinear nonlocal flat optics

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OPTICAL MATERIALS EXPRESS 2024年第1期14卷 92-100页

作者： de Ceglia, Domenico Alu, Andrea Neshev, Dragomir n. De Angelis, Costantino Univ Brescia CNIT Via Branze 38 I-25123 Brescia Italy Univ Brescia Dept Informat Engn Via Branze 38 I-25123 Brescia Italy CNR Ist Nazl Ott Via Branze 45 I-25123 Brescia Italy City Univ New York Adv Sci Res Ctr Photon Initiat New York NY 10031 USA City Univ New York Grad Ctr Phys Program New York NY 10016 USA Australian Natl Univ ARC Ctr Excellence Transformat Metaopt Syst TMOS Res Sch Phys Canberra ACT 2601 Australia

Digital signal processing has revolutionized many fields of science and engineering, but it still shows critical limits, mainly related to the complexity, power consumption, and limited speed of analogue-to-digital converters. A long-sought solution to overcome these hurdles is optical analog computing. In this regard, flat optics has been recently unveiled as a powerful platform to perform data processing in real-time, with low power consumption and a small footprint. So far, these explorations have been mainly limited to linear optics. Arguably, significantly more impact may be garnered from pushing this operation towards nonlinear processing of the incoming signals. In this context, we demonstrate here that nonlinear phenomena combined with engineered nonlocality in flat optics devices can be leveraged to synthesize Volterra kernels able to outperform linear optical analog image processing.

关键词： Analog to digital converters Edge detection Machine vision Optical computing Optical imaging Signal processing

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A nonlinear, Regularized, and Data-independent Modulation for Continuously Interactive image processing Network

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2024年第1期132卷 74-94页

作者： Lee, Hyeongmin Kim, Taeoh Son, Hanbin Baek, Sangwook Cheon, Minsu Lee, Sangyoun Yonsei Univ Seoul South Korea NAVER Cloud Corp Seongnam Gyeonggi Do South Korea Samsung Elect Co Ltd Suwon Gyeonggi Do South Korea Samsung Res Seoul South Korea Telofarm Seoul South Korea

Most studies on convolutional Neural Network (CNN) based image processing have proposed networks that can be optimized for a single level. Here, the term "level" refers to the specific objective defined for each task, such as the degree of noise in denoising tasks. Hence, they underperform on other levels and must be retrained to deliver optimal performance. Using multiple models to cover multiple levels involves very high computational costs. To solve these problems, recent approaches train the networks on two different levels and propose their own modulation methods to enable the arbitrary intermediate levels. However, many of them 1) have difficulty adapting from one level to the other, 2) suffer from unintended artifacts in the intermediate levels, or 3) require large memory and computational cost. In this paper, we propose a novel framework using Filter Transition Network (FTN), which is a non-linear module that easily adapts to new levels, is regularized to prevent undesirable side-effects, and extremely lightweight being a data-independent module. Additionally, for stable learning of FTN, we newly propose a method to initialize nonlinear CNNs with identity mappings. Extensive results for various image processing tasks indicate that the performance of FTN is stable regarding adaptation and modulation and is comparable to that of the other heavy frameworks.

关键词： image processing Network modulation Controllable neural networks Low level vision

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Class of nonlinear filtering and windowing methods for image processing and reconstruction

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OPTICS LETTERS 2025年第2期50卷 345-348页

作者： Soldati, Tatiana Thakur, Sarvesh a. De Boer, Johannes F. Hillmann, Dierck Vrije Univ Amsterdam Dept Phys NL-1081 HV Amsterdam Netherlands

Linear digital filters are at the core of image reconstruction and processing for many coherent optical imaging techniques, such as digital holography (DH) or optical coherence tomography (OCT). They can also be efficiently implemented using fast Fourier transform (FFT) with appropriate transfer/filter functions that operate in the frequency domain. However, even with optimal filter design, they suffer from side effects such as sidelobe generation or resolution limitations, e.g., when using windowing for apodization. Here, we propose a novel, to the best of our knowledge, nonlinear (NL) filter and apply it to coherent (complex-valued) data. The NL filter preserves correct phase information, reduces sidelobes, and can be implemented indirectly by linear filters, making it almost as performant as linear filters. We demonstrate the usefulness of these filters in OCT data processing, showing the separation of background from signal, and an alternative to classical windowing that retains the full width at half maximum (FWHM) resolution of a rectangular window function, but with sidelobe suppression comparable to other window functions. Contrary to many alternatives, it even performs well in proximity to scattering structures that are subject to speckle noise. Because of its simplicity and advantages, we expect to see widespread applications of the technique beyond the demonstrated applications.

关键词： Fourier transforms image reconstruction Imaging techniques Optical imaging Speckle noise Synthetic aperture radar

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Lattice Boltzmann Model for nonlinear Anisotropic Diffusion with Applications to image processing

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DOKLADY MATHEMATICS 2024年第3期110卷 464-468页

作者： Ilyin, O. V. Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow Russia

It is shown that the multiple nonconstant relaxation time lattice Boltzmann equation for five discrete velocities is equivalent in the diffusion limit to a nonlinear anisotropic diffusion equation. The proposed model ... 详细信息

关键词： lattice Boltzmann equations nonlinear anisotropic diffusion Perona-Malik equations

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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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Reservoir computing for image processing based on ion-gated flexible organic transistors with nonlinear synaptic dynamics

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ORGANIC ELECTRONICS 2025年 139卷

作者： Zhu, Li Wan, Xiang Lin, Junchen Chen, Pengyu Luo, Zhongzhong Sun, Huabin Yan, Shancheng Tan, Chee Leong Yu, Zhihao Xu, Yong Nanjing Univ Posts & Telecommun Coll Integrated Circuit Sci & Engn Nanjing 210023 Peoples R China Guangdong Greater Bay Area Inst Integrated Circuit Guangzhou 510535 Peoples R China Nanjing Univ Posts & Telecommun Coll Elect & Opt Engn Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Flexible Elect Future Technol State Key Lab Organ Elect & Informat Displays Nanjing 210023 Peoples R China

Reservoir Computing (RC) is an efficient framework for processing sequential data. It captures the dynamic features of complex data through high-dimensional mapping, significantly enhancing machine learning capability. However, due to the lack of suitable materials and device fabrication processes, developing highly energyefficient, flexible, and wearable RC systems remains a challenging task. In this paper, flexible ion-gated transistors were fabricated by spin-coating process at room temperature, utilizing polyimide (PI) as the flexible substrate, polyvinyl alcohol (PVA) doped with lithium perchlorate (LiClO4) as the gate dielectric, and poly [(bithiophene)-alternate-(2,5-di(2-octyldodecyl)-3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) as the organic semiconductor. Based on the electric double-layer (EDL) coupling in ion-gated transistors and the rich ionic dynamics, the device can simulate nonlinear synaptic functions such as excitatory postsynaptic current (EPSC), multi-pulse facilitation, and learning-forgetting-relearning behaviors. Based on the transistors' nonlinear synaptic functions, the constructed RC system can enhance image features while reducing the image size by half, effectively extracting and amplifying hidden features in the original images. In the handwritten digit recognition task, the RC system improved the recognition rate from 79.8 % to 90.6 %, compared to an Artificial Neural Network (ANN) of the same scale. The effective combination of flexible organic ion-gated transistors and the RC framework will undoubtedly contribute to the further development of the next generation of wearable intelligent systems.

关键词： Flexible organic transistor Artificial synapse Electric-double-layer Reservoir computing

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