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Comparison of Widely-used image Restoration Methods for Additive and Multiplicative Noises

Comparison of Widely-used Image Restoration Methods for Addi...

引用

Digital signal processing and its applications (DSPA), International conference on

作者： Mohammad Zedan Yury S. Bekhtin dept. Diagnostic Information Technologies National Research University “MPEI” Moscow Russian Federation dept. Automatics and Information Technologies in Control Ryazan State Radio Engieneering University named after V.F. Utkin Ryazan Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

This paper evaluates and compares the performance of various image denoising techniques, including wavelet transform, Wiener filtering, non-local means (NLM), denoising autoencoders (DAEs), and feedforward autoencoders. There are cases of additive and multiplicative noises considered where the Gaussian distribution with zero mean stands for the first case and an exponential distribution with unity mean describes the second case (speckle). The experiments have been conducted on the given dataset which contained both still test images and synthetic aperture radar (SAR) images with varying noise levels to analyze the effectiveness of these denoising approaches. The performance of each method is assessed using key image quality metrics: Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Squared Error (MSE), and Normalized Absolute Error (NAK). The results demonstrate that denoising autoencoders DAEs provide superior performance in terms of structural preservation and noise reduction, particularly in low-noise conditions, while Wiener filtering proves to be computationally efficient and robust under high-noise conditions. These findings offer insights into the selection of effective denoising techniques for noisy images based on specific noise characteristics and application requirements.

关键词： wavelet transforms Wiener filters Additives PSNR Autoencoders Speckle Radar polarimetry Computational efficiency image restoration Noise level

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A Convolutional Recurrent Mixer Network For Radar Meteorological image Super-Resolution

A Convolutional Recurrent Mixer Network For Radar Meteorolog...

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2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Pires, Rafael Gonçalves Santos, Daniel F.S. Calheiros, Roberto v. Papa, João Paulo Lee, Ik Hyun Bakshi, Sambit Muhammad, Khan Department of Computing São Paulo State University Bauru Brazil Meteorological Research Institute São Paulo State University Bauru Brazil Dept. of Mechatronics Engineering Tech University of Korea IKLAB Inc. Korea Republic of Dept. of CS&E National Institute of Technology Rourkela India School of Convergence Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9798350368741

image super-resolution (SR) focuses on reconstructing high-resolution images from their low-resolution counterparts, often affected by sensor limitations or environmental factors. Convolutional Neural Networks (CNNs) are state-of-the-art for SR tasks but computationally heavy. This paper introduces a novel CRMN (Convolutional Recurrent Mixer Network), a hybrid deep learning-based SR technique designed to address the complexity of CNNs, which is validated in the context of meteorological radar images. Experiments on public benchmark datasets (Berkley432 and T291) and our newly manually collected precipitation dataset from the Meteorological Research Institute (IPMET) show that our CRMN model provides competitive results compared to leading SR methods with significantly fewer parameters, making it a promising and practical solution for SR applications, particularly radar meteorology. © 2025 IEEE.

关键词： Convolutional Neural Networks Deep Learning Meteorology Recurrent Mixer Network Super Resolution

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PhysID: Physics-based Interactive Dynamics from a Single-view image

PhysID: Physics-based Interactive Dynamics from a Single-vie...

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2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Gothe, Sourabh vasant Chattopadhyay, Ayon Kiran, Gunturi venkata Sai Phani Pratik Agarwal, vibhav vachhani, Jayesh Rajkumar Ghosh, Sourav Parameswaranath, v.M. Barath Raj, K.R. Samsung R&D Institute India - Bangalore India

ISBN: (纸本)9798350368741

Transforming static images into interactive experiences remains a challenging task in computer vision. Tackling this challenge holds the potential to elevate mobile user experiences, notably through interactive and AR/vR applications. Current approaches aim to achieve this either using pre-recorded video responses or requiring multi-view images as input. In this paper, we present PhysID, that streamlines the creation of physics-based interactive dynamics from a single-view image by leveraging large generative models for 3D mesh generation and physical property prediction. This significantly reduces the expertise required for engineering-intensive tasks like 3D modeling and intrinsic property calibration, enabling the process to be scaled with minimal manual intervention. We integrate an on-device physics-based engine for physically plausible real-time rendering with user interactions. PhysID represents a leap forward in mobile-based interactive dynamics, offering real-time, non-deterministic interactions and user-personalization with efficient on-device memory consumption. Experiments evaluate the zero-shot capabilities of various Multimodal Large Language Models (MLLMs) on diverse tasks and the performance of 3D reconstruction models. These results demonstrate the cohesive functioning of all modules within the end-to-end framework, contributing to its effectiveness. © 2025 IEEE.

关键词： 3D Modelling Diffusion Multimodal Large Language Models Physics-Based Rendering

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FPGA Implementation of Optimized Floating Point Multiplier Using Minimal Error Logarithmic Approach 5th

FPGA Implementation of Optimized Floating Point Multiplier U...

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Basha, Sheik Jameer Eswar, Rakurthi Tharun Sai vidya, Talluri Sai Kumar, Sannapu Aravind Afreen, Shaik Satyanarayana, B.v.v. ECE Department Vishnu Institute of Technology Bhimavaram534202 India

ISBN: (纸本)9783031770777

Floating numbers are represented using IEEE standard 754. IEEE 754 defines formats for representing single-precision and double-precision floating-point numbers, along with rules for performing basic arithmetic operations on these numbers. The standard specifies the following components for both 3single-precision (32-bit) and double-precision (64-bit) floating-point numbers. This project introduces an efficient approach to IEEE 754 floating-point multiplication by implementing it in the logarithmic domain using Logarithmic Number System (LNS). It overcomes the limitations of the traditional floating pointing multipliers. By utilizing logarithmic and antilogarithmic converters, the logarithmic multiplier allows multiplication through addition, enabling support for higher accuracy levels. Floating point multipliers play a vital role in high-power computing applications like image and signal processing. This project approach presents a providing solution to the challenges posed by floating-point multiplication, offering improved performance, reduced delay, and low power consumption for demanding computational tasks in various applications. The multiplier is implemented using verilog HDL, targeted on Spartan-3E. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： image enhancement

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An Improved Digital signal processing for signal Enhancement and Interference Mitigation

An Improved Digital Signal Processing for Signal Enhancement...

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Automation and Computation (AUTOCOM), International conference on

作者： Intekhab Alam Paramjit Baxi Barnali Sahoo Mohan Garg Roopashree R Thaj Mary Delsy T Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Sciences Chemistry and Biochemistry JAIN (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Digital signal processing has become a fundamental tool in signal processing applications, such as speech and image processing, due to its ability to manipulate and examine signals in a digital form skillfully. A primary challenge in signal processing involves the presence of unwanted noise and interference, which can negatively impact the precision and quality of the processed signal. The proposed advanced DSP approach aims to improve signal enhancement and interference mitigation techniques using sophisticated algorithms and methods. The proposed state-of-the-art DSP approach includes noise reduction algorithms, such as adaptive filtering and spectral subtraction, to eliminate unwanted noise from the signal. Additionally, cutting-edge signal processing techniques, such as wavelet transforms and time-frequency analysis, are used for signal enhancement and demising. The proposed approach employs adaptive equalization techniques to mitigate interference from other signals and substantially improve the overall signal quality. The enhanced DSP approach is evaluated through simulation and real-world experiments, demonstrating its effectiveness in improving signal quality and reducing interference considerably. The proposed approach has the potential to significantly improve the accuracy and reliability of various signal processing applications considerably, making it a valuable tool in fields such as telecommunications, audio and video processing, and medical signal analysis.

关键词： Wireless communication wavelet transforms Time-frequency analysis Accuracy Prevention and mitigation signal processing algorithms Interference Digital signal processing wavelet analysis Telecommunications

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A Hybrid signal Denoising Approach using wavelet Decomposition and Neural Network-based Thresholding

A Hybrid Signal Denoising Approach using Wavelet Decompositi...

引用

Trends in Material Science and Inventive Materials (ICTMIM), International conference on

作者： P. v. Ramana Gadi Jaisindh Reddy vadla Sai Krishna Yamasani Yugandhar Reddy veeramreddy varun Reddy Academics Department of ECE Mohan Babu University Tirupati India ECE Sree Vidyaniekthan Engineering College Tirupati India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

signal denoising is a most important and fundamental task in signal processing. This is crucial for improving the quality of noisy data in applications like communication systems, audio processing, and biomedical signal analysis. While traditional methods such as wavelet Thresholding and Wiener Filtering have been effective in reducing noise and also pose a challenge in balancing noise removal and signal preservation. This can be particularly observed in the presence of non-stationary noise. This paper presents a novel hybrid denoising approach that integrates wavelet Decomposition with a Neural Network-based Thresholding. Initially the process starts with decomposing the noisy signal using the Discrete wavelet Transform (DWT). Then it is applied to a neural network to adaptively threshold the approximation coefficients. Such a process preserves detail coefficients. The denoised approximation is then used to reconstruct the signal, ensuring that both noise reduction and fine details are maintained. Experimental results show that the proposed method outperforms traditional techniques in terms of performance metrics. Mean Squared Error (MSE) for the proposed method is reduced to 0.22239, while the signal-to-Noise Ratio (SNR) increases to 3.5143 dB, and the Peak signal-to-Noise Ratio (PSNR) reaches 6.5289 dB. The Structural Similarity Index (SSIM) is 0.37442, indicating good structural preservation. In comparison, wavelet Thresholding and Wiener Filtering achieve higher MSE values and lower SNR. Additionally, the Cosine Similarity of the denoised signal is 0.82984, suggesting a strong resemblance to the clean signal. The proposed technique shows promise for real-time denoising applications, offering superior performance in terms of both noise removal and signal preservation.

关键词： Materials science and technology Wiener filters PSNR Neural networks Real-time systems Discrete wavelet transforms signal denoising Noise measurement Indexes signal analysis

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A Speech Enhancement Algorithm Combining wavelet Transform and Adaptive Filters

A Speech Enhancement Algorithm Combining Wavelet Transform a...

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International image processing, applications and Systems conference (IPAS)

作者： Aayam Shrestha Seyed Ghorshi Marjan Joorabchi Issa Panahi Fereshteh Fakhar Firouzeh Department of Electrical and Computer Engineering The University of Texas at Tyler Texas USA School of Science and Engineering Sharif University of Technology Iran Department of Electrical and Computer Engineering The University of Texas at Dallas Texas USA

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Noise as an unwanted interference can significantly degrade speech signals, especially those recorded by many microphones. This interference is modeled as additive noise that originates from a range of sources including White Gaussian Noise (WGN), babble, crowd, large city, and traffic noises. These disturbances can alter the characteristics of speech signals reducing both their quality and intelligibility. This paper introduces a novel approach designed to reduce noise and enhance the quality and intelligibility of speech signals. The proposed method combines wavelet Transform with Adaptive Filters, specifically the Wiener filter and RLS filter. The evaluation process involves testing noisy speech signals under realistic conditions with different signal-to-noise ratios (SNRs) and different types of additive noise. The objective measure is used for evaluation, including the perceptual evaluation of speech quality (PESQ). Results show that combining Wiener or RLS filtering with wavelet Transform significantly improves noise reduction, outperforming the use of wavelet Transform alone.

关键词： wavelet transforms Additive noise Wiener filters Filtering Noise Urban areas Adaptive filters Interference Speech enhancement wavelet analysis

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Investigation of Temporal Regularities of Accelerometer signals of Patients Suffering from Gait Freezing in Parkinson's Disease

Investigation of Temporal Regularities of Accelerometer Sign...

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Digital signal processing and its applications (DSPA), International conference on

作者： Anna v. Gurgenidze Olga S. Sushkova Margarita N. Khokhlova Alexei A. Morozov Kotelnikov IRE RAS Moscow Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

In this paper, the temporal properties of 3D-accelerometer signals were investigated. The signals were processed using the method of wave train electrical activity analysis. It is based on the analysis of wave train parameters. The wave train is localized increases in the signal's power spectral density over space and time. Using AUC (Area Under ROC Curve), regions in the parameter space were identified that contain differences in the number of wave trains in files with freezing of gait and without freezing of gait. It was found that AUC changes 10–20 seconds before the onset of the freezing of gait. This fact allows us to predict the freezing of gait.

关键词： Accelerometers Parkinson's disease Digital signal processing wavelet analysis Spectrogram

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An Improved Digital signal processing for signal Enhancement and Interference Mitigation

An Improved Digital Signal Processing for Signal Enhancement...

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3rd IEEE International conference on Automation and Computation, AUTOCOM 2025

作者： Alam, Intekhab Baxi, Paramjit Sahoo, Barnali Garg, Mohan Roopashree, R. Thaj Mary Delsy, T. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331542375

关键词： image enhancement

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Multiresolution Encoder-Decoder Convolutional Neural Network for Magnetic Resonance image Segmentation

Multiresolution Encoder-Decoder Convolutional Neural Network...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Kishore K. Tarafdar Aaditya Meher Mirat Shah Qutubuddin Saifee Dushyant Kumar Anant v. Nimkar Rama Jayasundar vikram M. Gadre Department of Electrical Engineering Indian Institute of Technology Bombay Department of Information Technology Sardar Patel Institute of Technology Mumbai LSI India R&D Pvt. Ltd. (Broadcom) Department of NMR All India Institute of Medical Sciences New Delhi Department of Computer Engineering Sardar Patel Institute of Technology Mumbai

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In image segmentation by deep learning, encoder-decoder Convolutional Neural Network (CNN) architectures are fundamental for creating and learning representations. However, with many filters in these models, one often loses the tractability of these models and interprets them as black boxes. In this paper, we describe the design of a multiresolution encoder-decoder CNN having a distinct low-frequency processing encoder and mixed-frequency processing decoder. The encoder of the proposed architecture first creates a localized spatial-frequency representation of the feature space using a framelet system and then learns representations using trainable convolution layers while converging to segment structures. The decoder takes in the encoder output, including distinct low and high-frequency skip connections and reconstructs the Region of Interest (RoI) with accurate boundaries. Spatial attention layers for low frequencies and a wavelet-based self-attention for high frequencies in the decoder successively aid the reconstruction of the desired segment. The described network architecture is overall lossless by design, despite information loss in wavelet pooling layers because of the initial subband decomposition and separate low and high-frequency paths. The model is tested separately using T1-weighted brain Magnetic Resonance (MR) images with two classes and accurately segments large and small RoIs. The trained model also successfully segments MR volumes acquired by our group.

关键词： image segmentation Magnetic resonance Network architecture Brain modeling Decoding Convolutional neural networks Spatial resolution Speech processing signal resolution image reconstruction

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