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A Quick Overview of Recent Developments in image Restoration using Machine learning

A Quick Overview of Recent Developments in Image Restoration...

引用

India Council International Subsections conference (INDISCON), ieee

作者： Neelam Kumari Isha Kansal Preeti Sharma Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

Digital images are gradually getting importance in every industry, including computer science, medical, communications, transport, security, and surveillance. Degraded images can be restored from a variety of distortions. Distortions occur due to malfunctioning imaging equipment transmission channels, atmospheric and environmental conditions, etc. image restoration techniques have applications across various domains like image processing, data mining, and predictive analysis. Today machine learning is used in almost every area by taking into consideration its excellence efficiency, decision making, to automate task and to retrieve pertinent data for analysis. In an effort to solve issues, numerous academics, mathematicians, and programmers have conducted research and used a variety of machine learning techniques. This paper centers on a comprehensive overview of machine learning, techniques used for image restoration methodologies. The purpose of the study is to compare evolution of various parameters like Peak Signal to Noise Ratio (PSNR), Structure Similarity Index Matrix (SSIM), Mean Structure Similarity Index Matrix (MSSIM), Mean Square Error (MSE) and Natural image Quality Evaluator (NIQE) between Machine Learning (ML) and traditional techniques.

关键词： PSNR Surveillance Noise reduction Machine learning Distortion image restoration Indexes Security Predictive analytics Smart phones

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MIF-SWT-AD: Medical image Fusion Using Stationary Wavelet Transform, Adaptive Feature Selection and Anisotropic Diffusion 3

MIF-SWT-AD: Medical Image Fusion Using Stationary Wavelet Tr...

引用

3rd ieee International conference on Device Intelligence, Computing and Communication Technologies, DICCT 2025

作者： Bhosekar, Shailesh Singh, Prabhishek Garg, Deepak School of Computer Science and Artificial Intelligence SR University Telangana Warangal India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798331543358

image fusion is a crucial technique in multi-modal imaging applications such as medical diagnostics, where integrating complementary information from different imaging modalities (e.g., Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Single Photon Emission Computed Tomography (SPECT), and Positron Emission Tomography (PET) etc. to improve clinical diagnosis and decision-making. This paper presents a hybrid image fusion method (MIF-SWT-AD) based on Stationary Wavelet Transform (SWT), incorporating Variance-Based Approximation coefficients (ACs) Fusion, Local Energy-Based Detail coefficients (DCs) Fusion, and Anisotropic Diffusion (AD) Filtering for post-processing step. The MIF-SWT-AD decomposes input images using SWT to obtain multi-resolution sub-bands (ACs, and DCs). The ACs are fused using a variance-based weighting strategy, ensuring dominant structural information retention, while the detail coefficients (DCs) are fused using local energy-based selection, preserving crucial edge and texture features. Finally, AD filtering is applied to the fused image for noise reduction and edge enhancement. The experimental results demonstrate that the proposed method achieves superior fusion quality compared to conventional fusion techniques, The proposed method effectively balances feature retention, noise suppression, and visual enhancement, making it a promising approach for high-quality image fusion applications. © 2025 ieee.

关键词： Positron emission tomography

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High-Frequency Transformer Network Based on Window Cross-Attention for Pansharpening

High-Frequency Transformer Network Based on Window Cross-Att...

引用

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

作者： Chengjie Ke Hao Liang Duidui Li Xin Tian Electronic Information School Wuhan University China China Centre for Resources Satellite Data and Application Beijing

Inspired by the powerful ability to capture long-distance dependencies in the vision transformer, we propose a novel high-frequency transformer network based on window cross-attention to fuse panchromatic (PAN) and multispectral (MS) images for a high-resolution MS image. To overcome the problem brought by shallow feature extraction in the previous transformer-based fusion network, we combine high-pass filtering and deep feature extraction to explore more texture information. As a result, the obtained relationship between MS and PAN images according to feature similarity is more accurate. In particular, we build the cross-modality correlation by a window cross-attention mechanism at pixel-level between MS and PAN images’ local window. Compared with patch-level, pixel-level helps to preserve fine-grained features. Therefore, more spatial details from a PAN image are transferred to an MS image, leading to a clearer fused MS image with good preservation of spectral information. Experimental results demonstrate that the proposed method outperforms the comparison methods in terms of visual and quantitative qualities.

关键词： visualization Correlation Satellites Fuses Filtering Pansharpening Signal processing

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An Accurate Segmentation Method for Experimental image of Cells 7

An Accurate Segmentation Method for Experimental Image of Ce...

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7th International conference on Computer and communications, ICCC 2021

作者： Li, Shoucong Chen, Weiyang School of Computer Science and Technology Jinan China

ISBN: (纸本)9781665409506

image segmentation is not only a basic problem in the field of image processing, but also in the field of biological data processing. In recent years, people have made a lot of efforts to improve the accuracy of segmentation. This paper proposes an image segmentation method based on generative adversarial networks to segment the images of human HT29 colon cancer cells. The network uses Unet as the generator network. The segmentation results of the human HT29 colon cancer cells image dataset show that compared with other methods, this method improves the average accuracy of image segmentation to 98.659%. © 2021 ieee.

关键词： image segmentation

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Reservoir-Based Distributed Machine Learning for Edge Operation of Emitter Identification

Reservoir-Based Distributed Machine Learning for Edge Operat...

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ieee communications Society (ComSoc)/AFCEA/ieee Military communications conference (MILCOM)

作者： Kokalj-Filipovic, Silvija Toliver, Paul Johnson, William Miller, Rob Peraton Labs Basking Ridge NJ 07920 USA

ISBN: (纸本)9781665439565

This paper has several contributions, all motivated by the operational aspects of in-situ retrainable Specific Emitter Identification (SEI) for authentication of mobile emitters at the Edge, tactical or IoT. The paper first provides a review of the prior work (DLR) that uses our design of reservoir delay loops (DL) to implement low-power, high accuracy and high-reliability classifiers of signals represented as time series of samples, capable of in-situ training at the Edge. We analyze those DLR properties that enable seamless authentication of mobile emitters on a larger scale using radio frequency (RF) fingerprints. Delay loops project the SEI inputs into a space where different input classes are linearly separable, allowing the use of a linear classifier for emitter identification. Moreover, the architecture of split loops enables a more effective linear separation, constraining the number of weight coefficients, which is important for efficient integration of locally trained DLRs into a global SEI model (D-DLR). D-DLR enables mobile edge platforms to authenticate and then track emitters. To authenticate mobile devices across large regions, D-DLR is trained in a distributed fashion with very little additional processing and a small communication cost, all while maintaining accuracy. We illustrate how to merge locally trained DLR SEI classifiers, and how to reliably detect unseen emitters using a simple multi-layer perceptron to which the DLR weights have been transferred.

关键词： Training image edge detection Time series analysis Neural networks Transfer learning Authentication Reservoirs

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Consumer Preference Estimation Using EEG Signals and Deep Learning

Consumer Preference Estimation Using EEG Signals and Deep Le...

引用

ieee Signal processing and communications Applications (SIU)

作者： Burak Ceylan Yalçın Çekiç Aydin Akan İstanbul Üniversitesi-Cerrahpaşa Bahçeşehir Üniversitesi İzmir Ekonomi Üniversitesi İstanbul Elektrik-Elektronik Mühendisliği Mekatronik Mühendisliği Elektrik-Elektronik Mühendisliği İstanbul İzmir Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Emotion estimation is an extremely critical and current research topic for human-computer interaction. In this study, a liking estimation method using electroencephalogram (EEG) signals is proposed to be used in neuromarketing studies. EEG data recorded while participants watch the advertisement videos of two different automobile brands are processed with deep learning techniques to estimate their liking status. After watching the videos, participants were presented with selected image sections from the advertisements (front view, console, side view, rear view, stop lamp, brand logo and front grille) and were asked to rate their liking by scoring from 1 to 5. EEG signals corresponding to these regions were converted into a two dimensional and RGB colored image using the short-time Fourier transform (STFT) method, and liking status was estimated using Deep Learning. The successful results obtained show that the proposed method can be used in neuromarketing studies.

关键词： Deep learning Human computer interaction Fourier transforms Estimation Neuromarketing Signal processing Electroencephalography

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Palm-vein images reconstruction against adversarial attacks

Palm-vein images reconstruction against adversarial attacks

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

作者： Lunke Fei Jiacheng Yang Wai-Keung Wong Shuping Zhao Anne Toomey Jiehang Deng The School of Computer Science and Technology Guangdong University of Technology Guangzhou China The Institute of Textiles and Clothing The Hong Kong Polytechnic University Hong Kong China The School of Design The Royal College of Art Kensington Gore South Kensington London UK

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Palm-vein has received widespread attention for reliable biometric recognition due to its robust resistance to replicate and forge. However, the rise of adversarial attacks poses a high risk of vulnerability for palm-vein recognition, leaving most existing methods vulnerable to small and human-imperceptible adversarial perturbations. In this paper, we propose a palm-vein image reconstruction network for palm-vein image protection, which mainly consists of palm-vein-specific exploration, feature refinement, and image reconstruction sub-networks. Specifically, we first specially learn the noise-insensitive palm-vein-specific feature by decoupling non-vein noise information via cascaded noise-injected and Canny-based convolution layers, and then refine palm-vein-specific features via multiple stacked basic convolution and transposed convolution pairs. Lastly, we convert the fine-grained palm-vein features into the latent sharp palm-vein images via two transposed convolution layers. Moreover, we develop both identity-aware and visual-aware loss functions to ensure the high-quality of the reconstructed palm-vein images. Experimental results on the widely used PolyU palm-vein dataset demonstrate the promising effectiveness of the proposed palm-vein image reconstruction network.

关键词： Representation learning visualization Convolution Perturbation methods Noise Speech recognition Reliability Speech processing Protection image reconstruction

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VoiceDiT: Dual-Condition Diffusion Transformer for Environment-Aware Speech Synthesis

VoiceDiT: Dual-Condition Diffusion Transformer for Environme...

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

作者： Jaemin Jung Junseok Ahn Chaeyoung Jung Tan Dat Nguyen Youngjoon Jang Joon Son Chung Korea Advanced Institute of Science and Technology South Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We present VoiceDiT, a multi-modal generative model for producing environment-aware speech and audio from text and visual prompts. While aligning speech with text is crucial for intelligible speech, achieving this alignment in noisy conditions remains a significant and underexplored challenge in the field. To address this, we present a novel audio generation pipeline named VoiceDiT. This pipeline includes three key components: (1) the creation of a large-scale synthetic speech dataset for pre-training and a refined real-world speech dataset for fine-tuning, (2) the Dual-DiT, a model designed to efficiently preserve aligned speech information while accurately reflecting environmental conditions, and (3) a diffusion-based image-to-Audio Translator that allows the model to bridge the gap between audio and image, facilitating the generation of environmental sound that aligns with the multi-modal prompts. Extensive experimental results demonstrate that VoiceDiT outperforms previous models on real-world datasets, showcasing significant improvements in both audio quality and modality integration. Synthesized samples are available on our demo page: https://***/projects/voicedit/

关键词： Adaptation models visualization Translation Pipelines Production Speech enhancement Signal processing Transformers Acoustics Text to speech

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Progressive DCT image Compression Based On Quad Tree Decomposition for WSN 2

Progressive DCT Image Compression Based On Quad Tree Decompo...

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2nd International conference on Advances in Engineering Science and Technology, AEST 2022

作者： Ayona, Sakena Abbas Al-Janabi, Ali Kadhim University of Kufa Najaf Department of Electronic and Communications Engineering Iraq

ISBN: (纸本)9798350334906

Wireless Sensor Networks (WSNs) have nodes with limited energy, limited processing power, and limited memory. In addition, they have low bandwidth and noisy communication channels. Therefore, image compression is very important for WSNs to reduce the time or the required bandwidth for transmitting the images. Progressive image compression is useful in WSNs because even when there is a cutoff in the transmitted data due to ending the battery lifetime or channel noise, the decoder can decompress the partially received data to obtain an acceptable image. Unfortunately, the progressive Discrete Cosine Transform (DCT) standard is unsuitable for WSNs because it uses the arithmetic coder or the Huffman coding that is very complex to implement. In this paper, we replace these coders with the Quad-Tree Decomposition (QTD) which has very low complexity. Unfortunately, most of the algorithms that use the QTD require lists to record the state of the image pixels and the quad-trees. These lists require an additional memory of about (2-3) of the image size. Therefore, it is not suitable for WSNs. In this research, we present the No List-QTD (NL-QTD) image compression algorithm that solves this memory constraint. It employs marker bits of an average memory of about 1/10 the image size instead of the lists to mark these states. The low complexity of the proposed NL-QTD also preserves the energy of the sensor's battery which in turn increases its lifetime. The simulation results show that the proposed method also has a higher Peak Signal-to-Noise Ratio (PSNR), and a higher Structural Similarity Index Measurement (SSIM), as compared with other related algorithms. © 2022 ieee.

关键词： image compression

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Bias Identification with RankPix Saliency

Bias Identification with RankPix Saliency

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

作者： Salamata Konate Léo Lebrat Rodrigo Santa Cruz Clinton Fookes Andrew Bradley Olivier Salvado CSIRO Data61 Brisbane Australia Queensland University of Technology Brisbane Australia

Saliency methods are critical tools that allow the estimation of the most important features of an input image that contribute to the network’s prediction. These tools are pivotal in high-stakes applications such as medical diagnosis or autonomous driving. Additionally, these tools can help identify models’ biasedness, such as a strong prior on object placement, easily distinguishable background features, or frequent object co-occurrence. We introduce RankPix, a novel saliency method for visual bias identification in image classification tasks. RankPix is a derivative-free approach that allows the identification of a minimum subset of pixels/features at a given network layer that changes the output of a classifier. Surprisingly, this approaches provides equivalent performance to gradient-based approaches on the standard pointing game benchmark. More interestingly, RankPix outperforms traditional approaches for systematic bias identification.

关键词： Training visualization Systematics Games Benchmark testing Signal processing Reliability

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