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46 articles for “Image reconstruction”
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Estimation Techniques in Image Restoration - A Survey Approach
Abstract: In the present paper a comparative study of various estimation techniques based on neural network, MATLAB, partial differential equation (PDE) and other proposed models for image restoration are been discussed. An image may be distorted, noisy or blurred and not suitable for extracting desired information or data, so it needs to be restored for desired application. Image restoration techniques are oriented towards modeling the degradation, blur and noise and applying …
Published in Current Trends in Signal Processing · Vol. 4, Issue 1, 2014 · pp. 11–16 Read article
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Early Alzheimer’s Disease Prediction Using Vision Transformers and Attention-Guided MRI Analysis
Abstract: Alzheimer’s Disease (AD) continues to be a major global health concern, with early detection being crucial for effective intervention. While conventional machine learning and convolutional neural network (CNN) approaches have made notable progress in automated AD diagnosis using MRI data, they often struggle with capturing long-range dependencies and maintaining spatial contextual awareness. In this research, we propose a novel framework using Vision Transformers (ViTs) for early Alzheimer’s prediction from 3D …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 1, 2026 · pp. 30–40 Read article
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Matrix Factorization and Tensor Decomposition at Scale: Mathematical Foundations and Computational Approaches
Abstract: Matrix factorization and tensor decomposition techniques have emerged as fundamental tools in machine learning and data science for handling high dimensional data efficiently. This paper presents a comprehensive analysis of scalable matrix factorization and tensor decomposition methods, focusing on their mathematical foundations, computational complexity, and practical applications. We examine key algorithms including Singular Value Decomposition (SVD), Non-negative Matrix Factorization (NMF), CP decomposition, and Tucker decomposition, with particular emphasis on their …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 56–59 Read article
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A Review on Fundamental Premises and Algorithmic Approaches in Compressive Sensing
Abstract: This paper addresses the classical approach of acquiring signals by following the well celebrated Shannon sampling theorem that underlies the majority devices of current technology, viz. analog-to-digital conversion, medical imaging, or audio and video electronics. In medical imaging, there are problems related to acquisition time and compression. Compressive sensing (CS) paradigm addresses the shortcomings of traditional data acquisition by sampling signals much more efficiently. CS is a novel kind of …
Published in Current Trends in Signal Processing · Vol. 6, Issue 1, 2016 · pp. 18–24 Read article
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Offline Handwritten Sanskrit Character Recognition System
Abstract: Despite technological improvements, computers still lag behind in language recognition. The majority of character recognition systems are incapable of reading cracked documents or handwritten characters or words. Sanskrit, an alphabetic script, is spoken by more than 100 million people worldwide. This study is about converting scanned handwriting images into text. This contains the steps below. The scanned image is initially segmented using a spatial space detection approach, after which the …
Published in Recent Trends in Sensor Research & Technology · Vol. 9, Issue 1, 2022 · pp. 32–37 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