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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Pandy, Saroj Kumar Kumar, Ankit Kumar, Gaurav Singh, Kamred Udham Singh, Teekam Srivastava, Sandeep Choudhury, Tanupriya Department of Computer Engineering and Applications GLA University UP Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789819732913

A method called picture fusion is largely concerned with improving photographs to enhance scene visualization. In order to produce a composite image that is more significant and instructive, it attempts to maintain the key elements from each image. This method has been used in a number of industries, including robots, satellite images, and medicine. Researcher decision-making and the success of scientific endeavors are greatly influenced by the quality of the fused image. In this article, a comparison of the wavelets Haar and Bior 2.2 is made. In order to compare the two wavelets, different photos are employed. Based on metrics like the Peak signal-to-Noise Ratio (PSNR), signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Standard Deviation (SD), the performance of the wavelets is assessed. Based on the results, it can be said that Bior 2.2 performs somewhat better than Haar in terms of picture fusion quality. We use orthogonal and biological wavelets to compare how well image fusion works. To fuse pictures, we utilize the Discrete Wavelet Transform (DWT) and the maximum selection criteria. The performance of the fused pictures is assessed using a number of factors, such as visual quality measurements. The advantages of biorthogonal wavelets over orthogonal wavelets for image fusion tasks are highlighted in this research, especially when working with numerous sensors. It emphasizes how important the decomposition level is for producing high-quality fused pictures. The results help advance image fusion methodologies and offer insightful information to academics and industry professionals involved in picture processing and analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Discrete wavelet transforms

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Samplet Basis Pursuit: Multiresolution Scattered Data Approximation With Sparsity Constraints

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 1813-1823页

作者： Baroli, Davide Harbrecht, Helmut Multerer, Michael Univ Svizzera Italiana Fac Informat CH-6962 Lugano Switzerland Univ Basel Dept Math & Comp Sci CH-4051 Basel Switzerland

We consider scattered data approximation in samplet coordinates with l(1)-regularization. The application of an l(1)-regularization term enforces sparsity of the coefficients with respect to the samplet basis. Samplets are wavelet-type signed measures, which are tailored to scattered data. Therefore, samplets enable the use of well-established multiresolution techniques on general scattered data sets. They provide similar properties as wavelets in terms of localization, multiresolution analysis, and data compression. By using the Riesz isometry, we embed samplets into reproducing kernel Hilbert spaces and discuss the properties of the resulting functions. We argue that the class of signals that are sparse with respect to the embedded samplet basis is considerably larger than the class of signals that are sparse with respect to the basis of kernel translates. Vice versa, every signal that is a linear combination of only a few kernel translates is sparse in samplet coordinates. We propose the rapid solution of the problem under consideration by combining soft-shrinkage with the semi-smooth Newton method. Leveraging on the sparse representation of kernel matrices in samplet coordinates, this approach converges faster than the fast iterative shrinkage thresholding algorithm and is feasible for large-scale data. Numerical benchmarks are presented and demonstrate the superiority of the multiresolution approach over the single-scale approach. As large-scale applications, the surface reconstruction from scattered data and the reconstruction of scattered temperature data using a dictionary of multiple kernels are considered.

关键词： Kernel signal resolution Sparse matrices Multiresolution analysis Hilbert space signal processing algorithms Dictionaries Basis pursuit multiresolution analysis samplets scattered data

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applications of Optical Mouse Sensor Other Than a Navigating Device: A Review

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IEEE SENSORS JOURNAL 2025年第6期25卷 9263-9272页

作者： Nayak, Shikha Das, Subir Chakraborty, Badal Meghnad Saha Inst Technol Dept Comp Sci & Engn Kolkata 700150 W Bengal India Natl Inst Technol Agartala Dept Elect & Instrumentat Engn Agartala 799046 Tripura India Bidhan Chandra Krishi Viswavidyalaya Fac Agr Engn Mohanpur 741252 W Bengal India

An optical mouse is a computer navigation device that uses light, primarily to track movement and control the cursor on a computer screen. Other than computer cursors, various fields of applications, such as process instrumentation, robotics, healthcare, and agriculture, now use this ready-to-use device as a sensor due to its low latency, lightweight, high accuracy, and power efficiency. It is also compatible with wearable devices and the Internet of Things (IoT)-based applications, which prefer low-power digital output. Typically, the optical mouse sensor gets popularity for those applications, where measurement is possible by precise motion data or analysis of object image. The sensor detects motion in both the x- and y-axes, and the built-in signal processing unit helps to interface with computers through wires and wireless data transfer protocols. This article's main contribution is a comprehensive study of optical mouse sensors used for various applications other than computer-pointing devices.

关键词： Optical sensors Sensors Optical imaging Optical signal processing Optical variables measurement Optical reflection Biomedical optical imaging image sensors Robot sensing systems Robots Healthcare Internet of Thing (IoT) optical mouse process instrumentation wearable device

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Graph Linear Canonical Transform: Definition, Vertex-Frequency Analysis and Filter Design

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 5691-5707页

作者： Chen, Jian Yi Zhang, Yu Li, Bing Zhao Beijing Inst Technol Sch Math & Stat Beijing 100081 Peoples R China Beijing Inst Technol Beijing Key Lab MCAACI Beijing 100081 Peoples R China

This paper proposes a graph linear canonical transform (GLCT) by decomposing the linear canonical parameter matrix into fractional Fourier transform, scale transform, and chirp modulation for graph signal processing. The GLCT enables adjustable smoothing modes, enhancing alignment with graph signals. Leveraging traditional fractional domain time-frequency analysis, we investigate vertex-frequency analysis in the graph linear canonical domain, aiming to overcome limitations in capturing local information. Filter design methods, including optimal design and learning with stochastic gradient descent, are analyzed and applied to image classification tasks. The proposed GLCT and vertex-frequency analysis present innovative approaches to signal processing challenges, with potential applications in various fields.

关键词： Filtering theory Transforms signal processing Matrix decomposition Laplace equations Band-pass filters Atoms Symmetric matrices Fourier transforms Eigenvalues and eigenfunctions Graph signal processing graph linear canonical transform vertex-frequency analysis filter design

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Comparative Analysis of Quantization Optimizations for Single-image Super-Resolution Models 32

Comparative Analysis of Quantization Optimizations for Singl...

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32nd IEEE signal processing and Communications applications Conference (SIU)

作者： Fisne, Alparslan Kalay, Alperen Bilecen, Bahri Batuhan ASELSAN Ankara Turkiye

ISBN: (纸本)9798350388978;9798350388961

This study describes how to increase inference performance without critical image quality loss by using post-training quantization methods for lightweight single-image superresolution (SISR) models. To observe this improvement, we compare the performance of the quantized models with the original models. Benchmark tests show that 8-bit quantization accelerates the original SISR models by 1.3x to 2.7x on an energy efficient GPU computing unit for various datasets. In our experiments, we compare the peak signal-to-noise ratio (PSNR) and inference times of commonly used activation functions using an interoperability framework. According to the test results, the quantized model with ReLU accelerates the inference compared to the quantized model that originally contained Tanh.

关键词： deep learning quantization image processing

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Low Power Multiplier Using Approximate Adder for Error Tolerant applications

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IETE JOURNAL OF RESEARCH 2025年第1期71卷 292-302页

作者： Hemanth, C. Sangeetha, R. G. Kademani, Sagar Shahbaz Ali, Meer Vellore Inst Technol Chennai Sch Elect Engn Chennai 600127 Tamilnadu India

In embedded applications and digital signal processing systems, multipliers are crucial components. In these applications, there is an increasing need for energy-efficient circuits. We use an approximate adder for error tolerance in the computational process to improve performance and reduce power consumption. Due to human perceptual constraints, computational errors do not significantly affect applications like image, audio, and video processing. Adiabatic logic (AL), which recycles energy, can also be used to build circuits that require less energy. In this work, we propose a carry save array multiplier employing an approximate adder based on CMOS logic and clocked CMOS adiabatic logic (CCAL) to conserve power. Additionally, using a precise full adder, multiplier parameters like average power and power delay product are calculated and compared with the multiplier. We performed simulations using 180 nm technology in Cadence Virtuoso.

关键词： CCAL CMOS Error tolerant Full adder Multiplier

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A New Approximate 5:2 Compressor for High Accuracy 8-Bit MAC Design

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IETE JOURNAL OF RESEARCH 2025年

作者： Nouri, Amirreza Aminian, Mahdi Ahmadifar, HamidReza Univ Guilan Dept Comp Engn Rasht *** Iran

The multiply-accumulate unit (MAC) is common in applications like as image and digital signal processing. In order to increase the speed and reduce the power consumption, approximate compressors can be used in the stages of computations. In this paper, three 5:2 approximate compressors are proposed and used in 8-bit MAC implementation. Proposed approximate MAC circuits were synthesized using Synopsys Design Compiler and compared with previous designs that used approximate circuits. Examining the error evaluation parameters including ER, MED and NMED shows that the proposed MUx_C3 design is 6%, 58.8% and 58.7% better than other existing designs, respectively. In performance-error rate compromising (PDP- $ NMED $ NMED), it has been shown that the proposed designs have the least error. To evaluate the quality of the results in image processing applications, the proposed approximate MAC were used in the image multiplication operation in MATLAB software. Measurement of SSIM and PSNR parameters show acceptable results.

关键词： 5:2 compressor approximate computing image processing application Multiply-Accumulate unit

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Dual-Band Imaging With an Integrated CMOS image Sensor-Liquid Crystal Shutter Device

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IEEE SENSORS JOURNAL 2025年第4期25卷 6250-6260页

作者： Skorka, Orit Mlinar, Marko Ispasoiu, Radu Onsemi San Jose CA 95134 USA Onsemi Ljubljana 1000 Slovenia

Dual-band imaging in the visible and near infrared (NIR) bands is used for remote sensing, surveillance, automotive, and other fields. CMOS image sensors (CISs), which are widely used in many imaging applications, can be used for dual-band imaging in visible and NIR because silicon is sensitive to light in these bands. There are various approaches for implementation of dual-band imaging systems. This article presents a single-sensor dual-band imaging approach that is based on integration of a CIS and a liquid crystal (LC) shutter. The proposed method is compared to dual-band imaging with an RGB-IR CIS, a single-sensor single-frame dual-band imaging approach that is commonly used at present. The comparison covers aspects that are related to image acquisition and image processing as well as image quality metrics and image artifacts. signal and noise metrics are explained using a theoretical model and are evaluated from actual images. The still images in this work were captured with a camera that included an RGB-IR image sensor and a camera that included an integrated CIS and a LC shutter device. images were captured in a controlled laboratory environment and outdoors. The images were used for a qualitative evaluation of the effects of relative resolution, stray light artifacts, and noise on end-user image quality, in addition to a quantitative evaluation of signal and noise properties. Advantages with each approach are summarized to highlight the system value-add of the two single-sensor dual-band imaging methods for certain applications.

关键词： Imaging Optical filters Dual band image sensors Band-pass filters Cameras image color analysis Hyperspectral imaging CMOS image sensors Synchronization CMOS image sensor (CIS) dual-band imaging hyperspectral imaging image sensor liquid crystal (LC) shutter multispectral imaging noise RGB-IR signal-to-noise ratio (SNR)

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Novel Wigner distribution and ambiguity function for the linear canonical transform with applications

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signal image AND VIDEO processing 2024年第11期18卷 8387-8401页

作者： Minh, Lai Tien Hanoi Architectural Univ Dept Math Hanoi 100000 Vietnam

This paper is to introduce new distributions associated with the linear canonical transform (LCT), which can be regarded as the generalization of the Wigner distribution (WD) and ambiguity function (AF). Moreover, some basic properties of the proposed distributions are also presented. Namely, the shift properties, the conjugation symmetry property, and the marginal properties are proved;the Moyal formula and the anti-derivative property are developed;and the relationship with the short-time Fourier transform (STFT) is also formulated. Furthermore, the newly defined distributions are shown to be valuable and effective in the detection of linear frequency-modulated (LFM) signals. They have better detection performance than the LW, and the LA but are also more flexible than the WDL, and the AFL.

关键词： Wigner distribution Ambiguity function Linear canonical transform Single-and multi-component LFM signal

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image Enhancement on Surveillance Footage Using Enhanced Deep Residual Network Super Resolution 8

Image Enhancement on Surveillance Footage Using Enhanced Dee...

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IEEE 8th International Conference on signal and image processing applications (IEEE ICSIPA)

作者： Omar, Zaid Daniel, Muhammad Haddid Rosbi, Munirah Univ Teknol Malaysia Fac Elect Engn Johor Baharu Malaysia

ISBN: (纸本)9798350352368

The study presents an approach for pedestrian detection from CCTV footage with the application of Enhanced Deep Residual Network Super Resolution (EDSR), in addition to YOLOv8 for person detection, to overcome blurry and low-resolution scenarios through image enhancement. For performance evaluation, Peak signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) are used to look at the pedestrian detection and super resolution performance, and compared to several other traditional enhancement methods namely bicubic, bilinear and nearest neighbor interpolation. Our positive results affirm the method's potential to be used in future enhancement tasks.

关键词： EDSR image enhancement super resolution surveillance

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