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9 articles for “Image denoising”
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A Hybrid model of ResNet50 integrated U-Net for image Denoising for Polymer and Composite Microstructure Analysis
Abstract: In digital era a high-quality imaging plays a very important role in polymer and composite material characterization features such as fiber-matrix interfaces, voids, microcracks cause problems in mechanical and functional properties. Polymer imaging includes optical microscopy and scanning electron microscopy, due to sensor limitation, environmental conditions add noise to the image and reduce quality of image. The noise degrades image quality, and it leads to reduce reliability in material analysis. …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 592–602 Read article
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Face And Spoofing Detection Via Genetic Algorithm-Based Feature Selection with MTCNN
Abstract: Face detection and liveness detection in various environments such as different lighting effects, occlusion, complex backgrounds, and different poses and angles play an important role in facial recognition or detection purposes. In this research paper, propose an improved algorithm for face detection and liveness, spoofing detection via genetic algorithm for feature selection, and mtcc detecting the edge of the facial image by Canny filter and spotting the face from the …
Published in Journal of Control & Instrumentation · Vol. 16, Issue 1, 2025 · pp. 17–26 Read article
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U-Net Based Approach for Automated Brain Tumor Classification
Abstract: Brain tumor detection and identification play vital roles in diagnostic procedures in the field of medicine, with the conventional analysis of MRI images requiring a lot of time and also subject to variability. The proposed study involves the use of a CNN-U-Net based approach for brain tumor detection and identification automatically. The study uses a database of 3,064 contrast-enhanced T1-weighted MRI images from 233 patients with the tumors of meningioma, …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 2, 2025 Read article
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Machine Learning in Nuclear Medical Applications: A Review of Research Frontiers
Abstract: Nuclear medicine, encompassing PET, SPECT, and targeted radionuclide therapy, generates high-dimensional, quantitative data uniquely suited for machine learning (ML) analysis. This review synthesizes current research applications of ML across six key domains. Positron emission tomography (PET), single-photon emission computed tomography (SPECT), and targeted radionuclide therapy are examples of nuclear medicine modalities that generate high- dimensional, quantitative datasets that are particularly well-suited for machine learning (ML)-driven analysis. These imaging methods provide …
Published in Journal of Nuclear Engineering & Technology · Vol. 16, Issue 1, 2026 · pp. 19–24 Read article
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Automated Blood Cell Counting and Disease Identification Using Image Processing: Implications for Polymer Composite- Based Biomedical Diagnostic Devices
Abstract: Accurate quantification of blood cells is central to clinical decision-making and to the performance of emerging polymer composite–based diagnostic platforms. This work presents a cost-effective, image-processing pipeline for automated counting of red blood cells (including overlapping cells), white blood cells, and platelets from Leishman-stained peripheral blood smears, and articulates its relevance to polymer composite microfluidic and biosensor devices. Implemented in Python with OpenCV, the workflow performs grayscale conversion, median/Gaussian denoising, …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 262–270 Read article
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Advancing Brain Tumor MRI Segmentation
Abstract: Segmentation of brain tumors in MRI scans is an integral part of neuroimaging carried out for diagnostic and therapeutic interventions. Given that manual segmentation is cumbersome and highly variable, there arises a need for automated, more precise segmentation solutions. This project, ‘Machine Learning and Deep Neural Networks to Advance Brain Tumor MRI Segmentation’ will develop a better, efficient, and accurate segmentation model to help clinicians identify brain tumors with greater …
Published in Recent Trends in Electronics Communication Systems · Vol. 12, Issue 2, 2025 · pp. 28–33 Read article
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Real-Time Edge Detection Camera Module Using Discrete Taylor Transform and Heat Equation (PDE): An Applied Mathematical Approach
Abstract: In modern digital signal processing, the capability for denoising and smoothing in real time is very important in scientific, engineering, and industrial applications. This paper presents an efficient hybrid framework that merges two mathematically sound methods, namely, DTT and PDE defined as the Heat Equation, to robustly denoise a signal with minimal distortion. The model addresses one of the most challenging tasks in signal restoration, which maintains the fidelity of …
Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 Read article
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Harnessing Deep Learning to Explore Microbial Community Structure and Carbon Storage Capacity in Mangrove Ecosystems: A Framework for Computationally
Abstract: Mangrove ecosystems represent one of the most efficient natural carbon sinks on Earth, functioning as critical blue carbon habitats that sustain diverse microbial communities responsible for biogeochemical cycling and long-term carbon storage. Despite their global ecological significance, accurately quantifying and predicting carbon sequestration in mangrove systems remains challenging due to the complex interactions between microbial diversity, sediment chemistry, and environmental drivers. This study presents a comprehensive and sustainable artificial intelligence …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 Read article
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Diffusion-Based Enhancement of Low-SNR Time- Frequency Signals
Abstract: Traditional enhancing techniques are useless in low signal-to-noise ratio (LSNR) situations because noise drastically interferes with communication signals. Based on an enhanced DiffBIR model, this paper suggests a dual-stage signal improvement approach that combines diffusion with deep learning. By combining the Inception module for multi-scale feature extraction with the Pixel Fusion Attention (PFA) module for significant region highlighting, the model improves signal recovery in the time- frequency domain. Experiments show …
Published in Current Trends in Signal Processing · Vol. 17, Issue 2, 2026 Read article