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Medical image Noise Reduction Methods Based on Cutting-Edge Hybrid Filtering Designs

Medical Image Noise Reduction Methods Based on Cutting-Edge ...

引用

International conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Saha, Tanusree Vishal, Kumar Magadh University Bodh Gaya India Department of Information Technology JIS College of Engineering Kalyani India Department of Mathematics Magadh University Bodh Gaya India

ISBN: (纸本)9789819613472

A high-resolution noise free medical image is an essential requirement for accurate disease detection. Medical image processing and analysis techniques have become utmost important in recent decades as a supplementary competence for medical professionals in the fields of diagnosis and prevention. Obtaining high-quality images without any interference is a crucial requirement for effectively managing therapeutic images. Medical images may contain many types of noises for involvement of simultaneous operation of multiple equipment involved in data capturing and transmission. Various filters have been suggested for eliminating different types of noises, each having its own merits and drawbacks depending on its capabilities and projected benefits. Our research depicts a technique for removing noises from medical images using a hybrid filtering approach based on ensemble adaptive mean filtering and Block-matching-3D filtering. The proposed methodologies specifically aim to reduce the speckle noise in brain MRI images obtained from ultrasonic therapy by employing hybrid filtering techniques. This proposed hybrid filtering algorithms integrate the most effective aspects of high-performance filters to achieve a de-noised image that retains fine details, edges, and textures. The statistical performance measurement metrics like Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR) are employed to evaluate the improved visual quality of the images. The results indicate that our proposed hybrid filtering technique functioning efficiently and provide better quality de-noised image compare with other existing de-noising techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image enhancement

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OmniFusion: Exemplar-Based Video Colorization Using OmniMotion and DifFusion Priors 17th

OmniFusion: Exemplar-Based Video Colorization Using OmniMoti...

引用

17th Asian conference on Computer Vision, ACCV 2024

作者： Fang, Xiaoyuan Dai, Longquan Tang, Jinhui Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9789819609161

Exemplar-based video colorization is a challenging task that involves the consistent propagation of colors across frames and the reasonable inference of colors from grayscale within frames. This paper proposes a novel video colorization method called OmniFusion, which iteratively completes the video colorization through two following steps. In the inter-frame propagation step, OmniMotion establishes correspondences between pixels across video frames. Any grayscale pixel can be queried whether a corresponding pixel and color are available from the exemplar according to such correspondences. Consequently, the processed images may still contain regions lacking color. In the intra-frame inpainting step, diffusion model provides these grayscale regions in a frame with plausible colors. The colorized frame is then fed into the first step as an exemplar, accepting queries from all uncolored pixels. This iterative process continues until all pixels are colorized. Evaluations indicate that OmniFusion achieves excellent performance in video colorization, surpassing existing methods in terms of color fidelity and visual quality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Color image processing

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Amodal Instance Segmentation with Diffusion Shape Prior Estimation 17th

Amodal Instance Segmentation with Diffusion Shape Prior Est...

引用

17th Asian conference on Computer Vision, ACCV 2024

作者： Tran, Minh Vo, Khoa Nguyen, Tri Le, Ngan University of Arkansas FayettevilleAR United States Coupang Inc. SeattleWA United States

ISBN: (纸本)9789819609710

Amodal Instance Segmentation (AIS) presents an intriguing challenge, including the segmentation prediction of both visible and occluded parts of objects within images. Previous methods have often relied on shape prior information gleaned from training data to enhance amodal segmentation. However, these approaches are susceptible to overfitting and disregard object category details. Recent advancements highlight the potential of conditioned diffusion models, pretrained on extensive datasets, to generate images from latent space. Drawing inspiration from this, we propose AISDiff with a Diffusion Shape Prior Estimation (DiffSP) module. AISDiff begins with the prediction of the visible segmentation mask and object category, alongside occlusion-aware processing through the prediction of occluding masks. Subsequently, these elements are inputted into our DiffSP module to infer the shape prior of the object. DiffSP utilizes conditioned diffusion models pretrained on extensive datasets to extract rich visual features for shape prior estimation. Additionally, we introduce the Shape Prior Amodal Predictor, which utilizes attention-based feature maps from the shape prior to refine amodal segmentation. Experiments across various AIS benchmarks demonstrate the effectiveness of our AISDiff. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image segmentation

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The Devil Is in the Details: Simple Remedies for image-to-LiDAR Representation Learning 17th

The Devil Is in the Details: Simple Remedies for Image-to...

引用

17th Asian conference on Computer Vision, ACCV 2024

作者： Jo, Wonjun Byung-Ki, Kwon Ji-Yeon, Kim Jeong, Hawook Joo, Kyungdon Oh, Tae-Hyun Department of Electrical Engineering POSTECH Pohang-si Korea Republic of Graduate School of AI POSTECH Pohang-si Korea Republic of Department of Convergence IT Engineering POSTECH Pohang-si Korea Republic of RideFlux Inc. Jeju-si Korea Republic of Artificial Intelligence Graduate School UNIST Ulsan Korea Republic of Institute for Convergence Research and Education in Advanced Technology Yonsei University Seoul Korea Republic of

ISBN: (纸本)9789819609680

LiDAR is a crucial sensor in autonomous driving, commonly used alongside cameras. By exploiting this camera-LiDAR setup and recent advances in image representation learning, prior studies have shown the promising potential of image-to-LiDAR distillation. These prior arts focus on the designs of their own losses to effectively distill the pre-trained 2D image representations into a 3D model. However, the other parts of the designs have been surprisingly unexplored. We find that fundamental design elements, e.g., the LiDAR coordinate system, quantization according to the existing input interface, and data utilization, are more critical than developing loss functions, which have been overlooked in prior works. In this work, we show that simple fixes to these designs notably outperform existing methods by 16% in 3D semantic segmentation on the nuScenes dataset and 13% in 3D object detection on the KITTI dataset in downstream task performance. We focus on overlooked design choices along the spatial and temporal axes. Spatially, prior work has used cylindrical coordinate and voxel sizes without considering their side effects yielded with a commonly deployed sparse convolution layer input interface, leading to spatial quantization errors in 3D models. Temporally, existing work has avoided cumbersome data curation by discarding unsynced data, limiting the use to only the small portion of data that is temporally synced across sensors. We analyze these effects and propose simple solutions for each overlooked aspect. Project page © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantic Segmentation

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VIPNet: Combining Viewpoint Information and Shape Priors for Instant Multi-view 3D Reconstruction 17th

VIPNet: Combining Viewpoint Information and Shape Priors fo...

引用

17th Asian conference on Computer Vision, ACCV 2024

作者： Ye, Weining Li, Zhixuan Jiang, Tingting School of Computer Science Peking University Beijing100871 China College of Computing and Data Science Nanyang Technological University Singapore Singapore National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science National Biomedical Imaging Center Peking University Beijing100871 China

ISBN: (纸本)9789819609680

While the multi-view 3D reconstruction task has made significant progress, existing methods simply fuse multi-view image features without effectively leveraging available auxiliary information, especially the viewpoint information for guiding and associating features of different views. To this end, we propose to enhance multi-view 3D reconstruction with the power of viewpoint information. Specifically, a simple-yet-effective viewpoint estimator is designed to learn and provide comprehensive viewpoint knowledge for locating and associating learned features from different views. Moreover, to improve the 3D reconstruction quality when 2D images of only very few viewpoints are available, we propose to learn the shape prior knowledge to provide sufficient shape information for compensating the limited 2D observations. Overall, we present VIPNet, benefiting from Viewpoint Information and Shape Prior learning for high-quality multi-view 3D reconstruction. Extensive experiments validate the effectiveness of the proposed VIPNet, which achieves state-of-the-art performance on challenging datasets and shows well generalization ability in real-world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D modeling

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Intelligent image Recognition and Classification Technology in Digital Media 1st

Intelligent Image Recognition and Classification Technology ...

引用

1st International conference on Pattern Analysis and Machine Intelligence, ICPAMI 2024

作者： Liang, Huan Guangzhou International Economics College Guangzhou510540 China

ISBN: (纸本)9789819633487

With the increasing popularity of digital media along with the fact that massive image data has already gained popularity, the automatic image recognition and classification have become a very critical and urgent problem, this study investigates and tests intelligent image recognition and classification techniques for digital media based on major methods such as CNN, SVM and RNN. Different algorithms and models are applied to training and test image data to evaluate the performance of various methods in terms of accuracy and processing time. The experimental results shows that the CNN method can achieve high accuracy in image classification task and the multi-layer convolution and pooling operations can effectively extract image features. The SVM method is better than the CNN in terms of processing time but have slightly lower accuracy. The RNN method is ranked between the CNN and SVM in terms of accuracy and processing time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image representation

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Enhanced Satellite image Fusion Using Deep Learning and Feature Extraction Techniques: A Survey 1st

Enhanced Satellite Image Fusion Using Deep Learning and Feat...

引用

1st International conference on Intelligent Systems in Computing and communications, ISCComm 2023

作者： Nallagachu, Swathi Sandanalakshmi, R. Department of Electronics and Communication Engineering Puducherry Technological University Puducherry India

ISBN: (纸本)9783031756047

This paper presents an overview and analysis of numerous research projects on image fusion methods, with a particular emphasis on deep learning-based methods. The research analyses the inadequacies of current fusion models and suggests novel methods for feature extraction, visual sensor networks, remote sensing applications, medical imaging, and multi-resolution image fusion. The study demonstrates the advantages of deep learning techniques for image fusion tasks, including Convolutional Neural Networks (CNNs), Stacked Auto encoders, and Convolutional Sparse Representation (CSR). These methods provide better fusion quality, fast processing, and better visual perception. Numerous studies provide novel methods, such as the rapid Integer Lifting Wavelet Transform (ILWT), Dual-Tree Complex Contourlet Transform (DT-CCT), and hybrid fusion methods that combine Discrete Cosine Transform (DCT) and Integer Lifting Wavelet Transform (ILWT) techniques. The review literature survey shows how these techniques can improve fusion outcomes while introducing little information distortion. In general, this paper brings together improvements in feature extraction and image fusion techniques, demonstrating the strength and potential of deep learning-based approaches. It is a useful tool for academics and professionals working in various fields who want to comprehend and advance these fields of study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image fusion

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Learning Systems in Forgery image Detection 1st

Learning Systems in Forgery Image Detection

引用

1st International conference on Intelligent Systems in Computing and communications, ISCComm 2023

作者： Shetty, Sudheer Ganesh, Devadiga Likhit kumar Poojary, Rahul R. Raviraj Tejas, R. Hegde, Nagaratna P. Vikkurty, Sireesha Department of ISE Sahyadri College of Engineering & Management Mangaluru Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of ISE Alvas Institute of Engineering and Technology Moodbidri Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of CSE Vasavi College of Engineering Telangana Hyderabad India

ISBN: (纸本)9783031756078

In an era dominated by the ubiquitous sharing of visual content on social media, the proliferation of fake photographs and digital fakes has emerged as a critical issue. This paper explores how fake images are detected with fusion of by various techniques of image processing, CNN, and machine learning, presenting an innovative approach to address the intricate challenge of identifying manipulated images. This paper gives insight on various approaches of image processing techniques and state-of-the-art machine learning algorithms used in a robust forgery image detection system. A comprehensive framework is proposed on various image forensic methods, ensuring the precise detection of fake images across diverse contexts. The paper also emphasizes the impact of countering the dissemination of fake photographs to preserve the credibility of digital media. This combination of image processing, CNN and machine learning propels the ongoing quest to uncover the truth behind the surge of digital fakes in today's digital landscape. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Design of AXI Bus-Based IP Core for image processing 12th

Design of AXI Bus-Based IP Core for Image Processing

引用

12th CCF conference on BigData, BigData 2024

作者： Chen, Yutong Yi, Zhongchao Li, Xuqiang Chen, Xingyan Chen, Yanjiang School of Software Engineering University of Science and Technology Hefei China School of Computer Science and Technology University of Science and Technology Hefei China

ISBN: (纸本)9789819610235

This study addresses the significant issue of image blur through image restoration techniques that reduce or remove blur and detect blurry regions, improving image quality and analysis potential. Utilizing FPGA-based digital hardware systems, the research leverages FPGA-supported systems like ZYNQ and ARM, using the Advanced eXtensible Interface (AXI) and Advanced Microcontroller Bus Architecture (AMBA) for streamlined device communication. The design incorporates specialized Intellectual Property (IP) cores such as Square Root (Sqrt) and Shift RAM, tailored to overcome the limitations of development cycles and processing algorithms, focusing on portability across various platforms including domestic FPGAs. The practical implementation features modules for axis-stream transmission, RGB to YUV conversion, image smoothing using Gaussian, mean, and median filters, advanced image processing with Sobel edge detection, and erosion-dilation modules. A ZYNQ verification system with an OV5640 camera and HDMI output substantiates the functionality and efficiency of the designed IP core, which is further analyzed for resource and power consumption using a High-Level Synthesis (HLS) tool. The study successfully demonstrates a robust IP core for blurry image processing, significantly enhancing the field of image processing and embedded systems, laying a strong groundwork for future advancements and porting endeavors, and highlighting the efficacy of FPGA-based image processing solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Median filters

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Relation-aware Semantic Alignment Network for Text-to-image Person Retrieval

Relation-aware Semantic Alignment Network for Text-to-Image ...

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

作者： Yong Wu Rongxi Zhou Hongchao Li Ze Zhou Feifei Wei Min Li Guodui He School of Computer and Information Anhui Normal University Wuhu China Guangxi Academy of Agricultural Sciences Nanning China China Communications Service Guangxi Technical Service Branch Nanning China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Text-to-image Person Retrieval (TIPR) aims to utilize natural language descriptions as queries to retrieve pedestrian images. However, existing methods only concentrated on aligning individual text-image pairs and ignored the specific self-representations within both visible images and textual descriptions of the same identity. This neglects the impact of intra-modal information distribution on TIPR. In this paper, a novel Relation-aware Semantic Alignment Network (RSAN) is proposed to learn reliable and comprehensive semantic visual-textual associations across different modalities. Specifically, A Global Semantic Alignment Matching (GSAM) loss is introduced to enhance the coherence of inter-modality features while preserving intra-modal representations for cross-modal matching. Additionally, an Adapter-assisted Information Aggregation (AIA) module is designed to further complement contextual information fusion between the image features and text embeddings. Extensive experiments conducted on two public benchmark datasets demonstrate the superiority of the proposed RSAN.

关键词： visualization Pedestrians Semantics Natural languages Text to image Coherence Benchmark testing Signal processing Reliability Speech processing

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