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5 articles for “Pixel level fusion”
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A Novel Approach of Medical Image Fusion using Wavelet Transforms
Abstract: Image processing applications have been growing rapidly in real world. The term fusion means an approach to extract the useful information from several modalities. Image fusion (IF) is used to integrate the complementary information obtained from multisensor, multiview and/or multitemporal and get an image of more information and the quality of which cannot be achieved from any individual image. Different fusion algorithms are useful in many applications like medical diagnosis …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 5, Issue 1, 2018 · pp. 18–25 Read article
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Comparative Review on Image Fusion Techniques
Abstract: The goal of image fusion is to combine relevant information from two or more images of the same scene of the different times. The result of image fusion is a new fused image which is more suitable for human being and machine discernment for further image-processing tasks like segmentation, feature taking out and object recognition. Image fusion is the combination of two or more different images to form a new …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 1, Issue 3, 2014 · pp. 10–13 Read article
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Real-Time Deepfake Detection in Video Conferencing Systems
Abstract: Deepfake technology presents non-exemplary threats to video conferencing platforms, enabling advanced fraud, impression and misinformation campaigns worth billions annually. Current detection methods either exhibit latencies exceeding 100ms or rely on server-side cloud processing, raising privacy concerns. This paper presents DeepConfGuard, a lightweight hybrid architecture combining MobileNetV2 for spatial feature extraction, a bidirectional LSTM with attention for temporal modelling, and EfficientNetV2 for refinement. It reaches 94.8% accuracy with 85 ms end‑to‑end …
Published in International Journal of Electronics Automation · Vol. 4, Issue 2, 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. 16, Issue 2, 2026 · pp. 15–27 Read article
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Dual-Stream Deep Learning Framework for Brain CT Image Classification and Implications for Polymer Composite Neuro Implant Evaluation
Abstract: Early and accurate classification of brain CT images is critical for diagnosing conditions such as aneurysms, tumors, and related lesions. We present a dual-stream image-classification framework that fuses convolutional neural network (CNN) features with handcrafted Histogram of Oriented Gradients (HOG) descriptors to jointly capture global semantics and local textural cues. The pipeline begins with modality unification via pixel-wise averaging to form a fused input, which is then processed in parallel …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 172–179 Read article