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Subspace Modeling Enabled High-Sensitivity x-Ray Chemical Imaging 48

Subspace Modeling Enabled High-Sensitivity X-Ray Chemical Im...

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48th IEEE International conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Li, Jizhou Chen, Bin Zan, Guibin Qian, Guannan Pianetta, Piero Liu, Yijin Stanford University Slac National Accelerator Laboratory United States City University of Hong Kong School of Data Science Hong Kong Max-Planck-Institut für Informatik Germany

ISBN: (纸本)9781728163277

Resolving morphological chemical phase transformations at the nanoscale is of vital importance to many scientific and industrial applications across various disciplines. The TxM-xANES imaging technique, by combining full-field transmission x-ray microscopy (TxM) and x-ray absorption near edge structure (xANES), has been an emerging tool that operates by acquiring a series of microscopy images with multi-energy x-rays and fitting to obtain the chemical map. Its capability, however, is limited by the poor signal-to-noise ratios due to system errors and low exposure illuminations for fast acquisition. In this work, by exploiting the intrinsic properties and subspace modeling of the TxM-xANES imaging data, we introduce a simple and robust denoising approach to improve the image quality, which enables fast and high-sensitivity chemical characterization. Extensive experiments on both synthetic and real datasets demonstrate the superior performance of the proposed method. © 2023 IEEE.

关键词： chemical imaging image restoration x-ray

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Super-Resolving SAR images with Diffusion Models: A Dual Evaluation of Metrics and applications 17

Super-Resolving SAR Images with Diffusion Models: A Dual Eva...

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17th IEEE International conference on signal processing, ICSP 2024

作者： Chen, Zilong Zhang, Siqian xiong, Boli National University of Defense Technology College of Electronic Science and Technology Changsha China

ISBN: (纸本)9798350387384

SAR, recognized for its capability to capture images under various weather conditions and at any time, is served as a key technology in remote sensing domains. Nonetheless, the inherent limitations of imaging systems often result in SAR images that do not achieve high resolution, thereby constraining their specific applications. Drawing inspiration from the efficacy of diffusion models in generative tasks involving natural images, we have devised a model that converts Gaussian noise into a super-resolution image derived from low-resolution input, utilizing a Markov chain framework and integrating residual prediction to facilitate faster convergence. The performance of this model is assessed in comparison to leading methodologies, including Generative Adversarial Networks and Transformers, employing both traditional evaluation metrics and semantic segmentation tasks. Experimental findings derived from the MSTAR and SARBake datasets reveal that the super-resolution algorithm utilizing a diffusion model exhibits superior performance relative to alternative models, as evidenced by conventional metrics. This algorithm not only demonstrates exceptional visual quality but also proves effective in downstream applications, underscoring its considerable potential for tasks involving super-resolution of SAR images. © 2024 IEEE.

关键词： Semantic Segmentation

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Improving Perturbation-based Explanation Methods with Application to Face Recognition 47

Improving Perturbation-based Explanation Methods with Applic...

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conference on applications of Digital image processing xLVII

作者： Lu, Yuhang Ebrahimi, Touradj Ecole Polytech Fed Lausanne EPFL Multimedia Signal Proc Grp MMSPG CH-1015 Lausanne Switzerland

ISBN: (纸本)9781510679344;9781510679351

In recent years, significant advancements in face recognition have been witnessed thanks to the rapid development of artificial intelligence. Despite remarkable performance, predictions made by such techniques tend to be challenging to explain. Considering their wide applications in security-sensitive areas, it is essential to fully understand the decision-making process of AI-based face recognition techniques and make them more acceptable to society. Many studies have been dedicated to offering visual interpretation for face recognition systems' decisions, such as generating similarity and dissimilarity saliency maps. One of the most promising approaches is based on the perturbation mechanism, which has demonstrated exceptional performance in highlighting similar regions between two matching face images. However, this type of method has shown to be less effective in identifying the dissimilar regions, which are particularly critical in the decision-making process for two non-matching face images. Therefore, this study focuses on the specific problem of the perturbation-based mechanism when applied to the explainable face recognition task. In particular, we first thoroughly analyze the limitation of a perturbation-based method in generating dissimilarity saliency maps. Then, a new regularization technique is proposed to alleviate this problem, followed by experiments to validate its effectiveness.

关键词： Explainable AI Face Recognition Saliency Map Perturbation-based Method

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Deconvolution-based image enhancement for optical coherence tomography 3

Deconvolution-based image enhancement for optical coherence ...

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conference on Biomedical Spectroscopy, Microscopy, and Imaging III

作者： Mendroch, Damian Bauer, Niklas Harings, David Heisterkamp, Alexander Cologne Univ Appl Sci Inst Appl Opt & Elect Betzdorfer Str 2 D-50679 Cologne Germany Leibniz Univ Hannover Inst Quantum Opt Welfengarten 1 D-30167 Hannover Germany

ISBN: (纸本)9781510673311;9781510673304

This work focuses on enhancing the quality of A- and B-scans of a novel linear optical coherence tomography system (LOCT), addressing the image degradation caused by noise and the blurring characteristics of the system's three-dimensional point spread function. The enhancement procedure includes an initial spatial and frequency-based pre-filtering that is applied to the measured interference pattern. Subsequently, a more robust envelope detection technique based on the Hilbert transform is employed. Lastly, image structures are reconstructed using a deconvolution algorithm based on maximum likelihood estimation, tailored to meet our unique requirements by adapting it to Rician distributed intensity values and employing a sparseness regularization term. For the deconvolution, both the lateral and axial blur of the system are considered. Emphasis is placed on the optimization of signal detection in high-noise regions, while simultaneously preventing image boundary artifacts. The efficacy of this approach is demonstrated across multiple types of measurement objects, including both artificial and biological samples. All results show a significant reduction in noise as well as enhanced resolution. Structure distinguishability is also increased, which plays a crucial role in tomography applications. In summary, the proposed enhancement method substantially improves image quality. This is achieved by still using the same initial measurement data, but incorporating prior knowledge and maximizing the amount of extracted information. Although initially designed for LOCT systems, the processing steps have potential for broader application in other types of optical coherence tomography and imaging systems.

关键词： Optical coherence tomography deconvolution filtering biomedical imaging signal processing

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Facial Deblurring and Recognition Using image processing and Machine Learning Techniques 8th

Facial Deblurring and Recognition Using Image Processing and...

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8th IFIP TC 12 International conference on Computer, Communication and signal processing, ICCCSP 2024

作者： Subrahmanyam, B.R. Janavi, T.S. Keerthana, V. Serajudeen, Ayesha Jumana Arunnagiri, A.M. Department of Electronics and Communication Engineering College of Engineering Guindy Anna University Chennai600025 India

ISBN: (纸本)9783031736162

The project addresses the challenge of accurately identifying blurred faces in computer vision and facial recognition. It introduces a novel framework that integrates deblurring techniques, utilizing point spread function deconvolution to enhance facial image quality. Principal Component Analysis (PCA) is employed for feature extraction, and a K-Nearest Neighbors (KNN) classifier is applied for face identification. The combined deblurring and PCA-transformed features improve matching and identification accuracy, particularly in scenarios with initially blurred images. Experimental validation on a real-world dataset demonstrates the efficacy of the proposed methodology. This approach not only enhances facial recognition accuracy but also lays the groundwork for future research in challenging practical applications, such as security and law enforcement. © IFIP International Federation for Information processing 2025.

关键词： Optical transfer function

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Innovative Methods for Low-Resolution image Recognition in Face and License Plate Tasks 4

Innovative Methods for Low-Resolution Image Recognition in F...

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4th International conference on signal processing and Computer Science, SPCS 2023

作者： Li, Jiangqi Chen, Yuhan Wang, xuelin Tan, Benying School of Artificial Intelligence Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9781510672505

Despite the great performance achieved by deep learning-based image recognition techniques in recent years, low-resolution image recognition is still challenging in terms of improving model performance and reducing cost consumption for practical applications. In this task, we propose a solution to achieve low-resolution image recognition by appropriately reducing the resolution of the images in the database so that there is a better match between them and the image to be recognized. In the task of face recognition, we use the Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) to determine whether the image contains a face or not, followed by the KNN model for face information recognition. In the number plate recognition task, we use the Convolutional Neural Network (CNN) model to recognize number plates. The experiments were validated on two publicly available datasets. The experimental results show that it can perform better in accomplishing low-resolution image recognition tasks while saving computational resources and other costs. © 2023 SPIE.

关键词： Convolutional neural networks

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EO and radar fusion for fine-grained target classification with a strong few-shot learning baseline 33

EO and radar fusion for fine-grained target classification w...

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conference on signal processing, Sensor/Information Fusion, and Target Recognition xxxIII

作者： Ballan, Luca Melo, Jorge G. O. van den Broek, Sebastiaan P. Baan, Jan Heslinga, Friso G. Huizinga, Wyke Dijk, Judith Dilo, Arta TNO Intelligent Imaging Oude Waalsdorperweg 63 NL-2597AK The Hague Netherlands

ISBN: (纸本)9781510674332;9781510674325

Combining data from multiple sensors to improve the overall robustness and reliability of a classification system has become crucial in many applications, from military surveillance and decision support, to autonomous driving, robotics, and medical imaging. This so-called sensor fusion is especially interesting for fine-grained target classification, in which very specific sub-categories (e.g. ship types) need to be distinguished, a task that can be challenging with data from a single modality. Typical modalities are electro-optical (EO) image sensors, that can provide rich visual details of an object of interest, and radar, that can yield additional spatial information. Defined by the approach used to combine data from these sensors, several fusion techniques exist. For example, late fusion can merge class probabilities outputted by separate processing pipelines dedicated to each of the individual sensor data. In particular, deep learning (DL) has been widely leveraged for EO image analysis, but typically requires a lot of data to adapt to the nuances of a fine-grained classification task. Recent advances in DL on foundation models have shown a high potential when dealing with in-domain data scarcity, especially in combination with few-shot learning. This paper presents a framework to effectively combine EO and radar sensor data, and shows how this method outperforms stand-alone single sensor methods for fine- grained target classification. We adopt a strong few-shot image classification baseline based on foundation models, which robustly handles the lack of in-domain data and exploits rich visual features. In addition, we investigate a weighted and a Bayesian fusion approach to combine target class probabilities outputted by the image classification model and radar kinematic features. Experiments with data acquired in a measurement campaign at the port of Rotterdam show that our fusion method improves on the classification performance of individual modalities.

关键词： deep learning sensor fusion image classification few-shot learning foundation models EO and radar decision support

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Multi-scale reconstruction generative networks for thermal to visible image transformation

Multi-scale reconstruction generative networks for thermal t...

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2023 International conference on image, signal processing, and Pattern Recognition, ISPP 2023

作者： xiang, Rui Li, Quanyin Huang, Yan Wang, Guoyou National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781510666351

In recent years, the field of thermal to visible image transformation has made great progress with Generative Adversarial Network(GAN). Since GANs own the ability to fit the signal frequency, these methods are limited to using more GANs or designing sub-tasks to improve the quality of the generated image. Due to the large difference in the information frequency of individual objects in the image, GANs pay less attention to low-frequency information, which leads to edge distortion of dynamic objects such as people and vehicles in the generated image. Edge distortion has become an important issue for practical applications We propose a new structure and two new loss functions to complete the task of thermal to visible image transformation. Our network takes one thermal image as input and generates a visible RGB image as output. First, we down-sample inputs for three times to get multi-scale inputs. Then we build outputs in multi-scale form small size to large size and fuse the multi-scale outputs. In train stage, our two new losses better direct the network to converge. In order to prove the effectiveness of the proposed thermal to visible image transformation algorithm, we use the KASIT-MPD dataset, which includes real paired thermal and visible images. © 2023 SPIE.

关键词： Generative adversarial networks

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Conceptual importance of Imaging and signal processing in the Medical Science 1

Conceptual importance of Imaging and Signal Processing in th...

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1st International conference on Healthcare Innovations, Software and Engineering Technologies, HISET 2024

作者： Yadav, Pratik Lokhande, Ashok Prasanna, B Lakshmi Thorat, Ganesh Jaywant College of Engineering and Polytechnic Department of E&tc Engineering Karad India Jaywant College of Management Karad India Institute of Aeronautical Engineering Department of C Department of Electronics and Communication Engineering Hyderabad India Deemed to Be University Department of Medicine Krishna Vishwa Vidyapeeth Karad India

ISBN: (纸本)9798350360707

Information technology has impacted every part of human life, and in the last few years, the fields of healthcare and medical science have felt the effects of this innovation. Biomedical images, which are defined as representations of the body that aid in understanding the nature of the biological systems, are an essential component of medical science. applications of biomedical engineering include the creation of pharmaceutical medications, regenerative tissue growth, therapeutic biology, common imaging devices like MRIs and EEGs, and diagnostics and therapeutics medical device ranging from clinical equipment's to micro-implant. x-rays, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and ultrasound are the most often utilized imaging modalities in medical applications. Improving the information illustrates interpretability is the only goal of medical image processing. Medical image analysis falls into the following primary categories: localization, detection, classification, segmentation, enhancement, and registration. Integrating imaging data with non-imaging data is the computational approach. Expert systems like neural networks and fuzzy logic are linked with image processing techniques including enhancement, segmentation, and restoration morphological systems to meet this problem. Future diagnosis should be made possible for doctors by the interoperability of essential algorithm integration and support for standard approaches. © 2024 IEEE.

关键词： Positron emission tomography

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An imageJ plugin for image fusion based on edge-preserving filtering 8

An ImageJ plugin for image fusion based on edge-preserving f...

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conference on Optics, Photonics, and Digital Technologies for Imaging applications VIII

作者： Singh, Harbinder Forero, Manuel C. Agaoglu, Nuray Bueno, Gloria Deniz, Oscar Cristobal, Gabriel Univ Castilla La Mancha VISILAB Ciudad Real Spain Univ Ibague Fac Ingn Grp D Tec Semillero Procesamiento Imagenes & Aprendizaje Ma Ibague Colombia CSIC Inst Opt Serrano 121 Madrid Spain

ISBN: (纸本)9781510673151;9781510673144

A novel imageJ plugin is designed to extend the depth-of-field (DoF) by seamlessly fusing a series of multi-focus images, allowing for in-depth analysis. Moreover, it has been tested on multi-exposure image stacks, demonstrating its adeptness in preserving intricate details within both poorly and brightly illuminated regions of 3-D specimens. The significance of this capability becomes particularly apparent when dealing with images that exhibit a limited DoF and varying exposure settings under low signal-to-noise ratio conditions. The plugin's effectiveness has been thoroughly validated through the processing and analysis of numerous image stacks featuring diverse diatom and cyanobacteria species. The proposed methodology incorporates a two-scale decomposition (TSD) scheme, complemented by the refinement of weight maps using edge-preserving filtering (EPF). This dual approach ensures the preservation of fine details in the fused image while simultaneously minimizing noise. Such innovations make this plugin a valuable tool for researchers and analysts working with complex image datasets.

关键词： Depth-of-field image fusion quality metrics noise imageJ edge-preserving filter (EPF) preservation of details multi-focus images

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