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121 articles for “image de-noising”
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Attribute based Level Adaptive Thresholding Algorithm for Object Extraction
Abstract: Image processing plays a vital role in the computer vision because most of the scenarios require object extraction and recognition. In order to utilize these images for the procedural analysis of the environmental entities in computational means, it must be a noiseless one. However, the images are affected through noises caused by the various acquisition techniques and hence an effective technique for denoising and segmentation is necessary in Computational Tomography. …
Published in Journal of Advancements in Robotics · Vol. 1, Issue 2, 2014 · pp. 13–17 Read article
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Detection and Retrieval of Follicles in Polycystic Ovarian Syndrome (PCOS) using Image Processing Techniques
Abstract: About to 18% women of reproductive age are affected by PCOS (Poly Cystic Ovarian Syndrome), a common endocrinopathy. It is also known as PCOS (Poly Cystic Ovarian Syndrome) or Stein-Leventhal syndrome. An imbalance of woman’s female hormones like estrogen and progesterone occur which causes the infertility. There are various methods which are used to diagnose this condition but the most helpful method is the pelvic ultrasound which confirms the presence …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 3, Issue 1, 2016 · pp. 1–6 Read article
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Optical Image Sensing and Analysis of Iron Ore Pellets: A Machine Learning Approach
Abstract: The present work is aimed to improve quality control in steel production using SEM imaging and machine learning. High-resolution SEM images of iron ore pellets, primarily composed of hematite and magnetite, are analyzed to understand their microstructural features, which significantly impact pellet performance during reduction processes. Traditional microstructure analysis is manual, time- consuming, and prone to inconsistencies. This study proposes an automated approach using K-Means Clustering, Canny Edge Detection, DBSCAN, …
Published in Journal of Instrumentation Technology & Innovations · Vol. 15, Issue 3, 2025 · pp. 7–18 Read article
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Evaluation of Image Reconstruction Techniques in Single Photon Emission Computed Tomography (SPECT/CT) Imaging
Abstract: AbstractThis work is aimed at evaluating the image reconstruction techniques used in single photon emission computed tomography (SPECT/CT) imaging. The reconstruction techniques considered in this work are: the ordered subset expectation maximization (OSEM) and the Filtered Back Projection (FBP). An anthropomorphic torso phantom with lung and liver inserts was injected with 99mTc. The liver and the background were given 0.2 MBq/ml and 0.07 MBq/ml respectively. The lungs inserts were filled …
Published in Research & Reviews : Journal of Physics · Vol. 5, Issue 2, 2016 · pp. 13–22 Read article
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Skin Disease prediction and classification from dermoscopy images using Neural Network
Abstract: Skin diseases are among the most common health-related problems affecting people of all age groups, and their occurrence often varies with seasonal and environmental conditions. Delayed or incorrect diagnosis of skin disorders can lead to severe complications, making early and accurate detection extremely important for effective treatment and prevention. In recent years, rapid advancements in deep learning and neural network technologies have significantly contributed to the development of automated medical …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 Read article
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A Multi-Quadrant, Integrated and Secure Model of Multiple Watermarking
Abstract: Watermarking safeguards the copyright of digital images with its original owner. A watermarking model is projected by conjoining three distinct multiple watermarking approaches. The secure model is obtained by integrating multiple watermarking with the latest secure cryptographic algorithms. The proposed model will improve copyright protection, achieve large embedding capacity and provide a stronger association amongst image and watermark for integrity and authentication. The multiple watermarking model partitions the cover image …
Published in Journal of Open Source Developments · Vol. 1, Issue 2, 2014 · pp. 15–28 Read article
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Cross-Domain Comparative Analysis of Microwave Imaging Systems for Medical Diagnostics and Industrial Testing
Abstract: Microwave imaging is gaining significant traction as a non-ionizing, low-cost, and portable alternative to conventional diagnostic and inspection modalities in both medical and industrial domains. Leveraging the dielectric contrast between healthy and anomalous tissues or materials, microwave imaging systems enable early-stage detection and characterization of pathological or structural anomalies. This review provides a detailed comparative analysis of microwave imaging systems tailored for three critical applications: breast cancer detection, brain stroke …
Published in Journal of Microwave Engineering and Technologies · Vol. 12, Issue 2, 2025 · pp. 39–48 Read article
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Overview and Performance Evaluation of BM3D Image Denosing
Abstract: We propose an information subordinate denoising technique to restore uproarious pictures. Not quite the same as existing denoising calculations which hunt down patches from either the boisterous picture or a nonexclusive image, the new calculation finds patches from an inputting image that contains applicable patches. We detail the denoising issue as an ideal channel plan issue and make two commitments: Initially, we decide the premise capacity of the solving so …
Published in Journal of Operating Systems Development & Trends · Vol. 3, Issue 2, 2016 · pp. 23–29 Read article
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Sensor Guard: Thermal Sensor System for Lion Detection and Collision Prevention
Abstract: This paper presents a novel system that utilizes thermal imaging and CCTV cameras integrated with sensors to detect heat sources, specifically focusing on the detection of lions near railway tracks to prevent collisions. The system employs infrared thermal imaging to identify heat signatures of animals, particularly lions, that may create risk to his/her life. By combining thermal sensors and machine learning algorithms, the system is capable of accurately the heat …
Published in Recent Trends in Sensor Research & Technology · Vol. 12, Issue 1, 2025 · pp. 21–28 Read article
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A Hybrid Image Codec Using Singular Value Decomposition and Adaptively Scanned Wavelet Difference Reduction Method
Abstract: AbstractIn this paper, a hybrid image codec for the gray scale images is proposed. It practices singular value decomposition (SVD) and adaptively scanned wavelet difference reduction (ASWDR) technique for compression. The proposed hybrid method retains the advantage of high peak signal to noise ratio (PSNR) value at a level of compression. The image is first compressed by SVD, which is a well-established approach to removing interpixel redundancies for reducing some …
Published in Journal of Open Source Developments · Vol. 4, Issue 1, 2017 · pp. 4–15 Read article
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A Statistical Approach Noise Tolerant Texture Classification
Abstract: A simple, efficient, yet robust multi resolution approach to texture classification binary rotation invariant and noise tolerant. The proposed approach is very fast to build and very compact while remaining robust to illumination variations, rotation changes, and noise. We have developed a novel and simple strategy to compute a local binary descriptor based on the conventional local binary pattern (LBP) approach, preserving the advantageous characteristics of uniform LBP. Points are …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 3, Issue 2, 2016 · pp. 14–19 Read article
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Deep Learning Based Detection and Classification of Brain Tumors Using MRI Images
Abstract: Brain tumor detection using magnetic resonance imaging (MRI) is a critical task in the early detection and treatment of brain tumors. Manual analysis of brain tumor detection using MRI is a tedious task that requires expertise in the field. Therefore, this study proposes a deep learning-based approach for brain tumor detection and classification using Convolutional Neural Networks (CNN). The proposed approach preprocesses the MRI image using normalization, resizing, and noise …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 Read article
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Person Identification Using Iris, Facial Image and Fingerprint with the Combined Algorithm of Principle Component Analysis and Singular Value Decomposition
Abstract: In this research, we consider three most important biometric features such as fingerprint, iris, and facial image to identify a person more precisely. Unimodal biometric system has several problems, such as noisy background data, non-universality and spoof attacks. Multimodal biometric systems can solve these limitations effectively by using two or more individual modalities. This system propose the Multimodal biometric systems on fingerprint, iris and facial image using combined algorithm of …
Published in Journal of Computer Technology & Applications · Vol. 8, Issue 1, 2017 · pp. 29–34 Read article
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Modified Morphology of Wavelet based Transformations for Color Image Compression
Abstract: For the sake of effective transmission and less storage of digital information the signal processing environment requires compression schemes to facilitate easy handling and management of large chunk of information. Such that the redundant data and other chunks of information which can be reproduced from the existing or basic information can be reproduced in no time without requiring large database storages. Wavelet processing is one of such methods which are …
Published in Journal of Advanced Database Management & Systems · Vol. 2, Issue 2, 2015 · pp. 1–9 Read article
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Design and Development of Screw Detection System : A case study
Abstract: This study explores the design of a vision-based screw detection and orientation system for industrial automation, inspection, and robot disassembly. By integrating machine learning algorithms like region-based convolutional neural networks (R-CNN) with traditional image processing and impedance sensing, the system performs real-time screw presence detection, head type identification, and alignment. Three key technologies—deep learning classification, edge-based geometric analysis, and impedance verification—are integrated into a single modular system. The findings indicate …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 4, Issue 1, 2026 · pp. 30–36 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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A study in Leveraging Deep Learning and IoT Arrays for Dynamic, Hyper-Local Atmospheric Intelligence
Abstract: The critical demand for high-resolution, actionable atmospheric data is challenged by the high cost and sparse coverage of traditional regulatory monitoring stations. This paper explores the synergistic paradigm shift enabled by integrating low-cost, dense Internet of Things (IoT) sensor arrays with advanced Artificial Intelligence (AI) methodologies, specifically Deep Learning (DL) models. We address the primary limitations of low-cost sensors—inherent bias, sensitivity to environmental drift (temperature/humidity), and calibration inconsistency—by utilizing AI …
Published in International Journal of Atmosphere · Vol. 2, Issue 2, 2025 · pp. 50–62 Read article
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Simulation of Image Processing and Restoration Through A Base Image Using Matlab
Abstract: Basically, in image processing the main work is done on pixels and its value. Basically, an image is a two-dimensional function f(x, y), where x and y are spatial coordinates. Here, in the present study geometrical transformation is being used for image restoration and processing. Steps are carried some for the result outcome. First the image is read in the algorithm, further the coordinates and pixels are being specified, and …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 8, Issue 1, 2021 · pp. 31–40 Read article
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A Comprehensive Review of CNN-Based Framework for Multi-Sign Detection of Diabetic Retinopathy in Fundus Images Using Public Datasets
Abstract: Diabetic retinopathy (DR) is one of the main causes of vision impairment. Blindness prevention and effective treatment depend on early detection. A thorough deep learning-based framework for the automatic segmentation and simultaneous detection of exudates, hemorrhages, and microaneurysms – three important DR indicators – from retinal fundus images is presented in this work. These three pathological signs’ corresponding annotated image patches, along with background (no-sign) areas, were used to train …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 3, 2025 · pp. 14–23 Read article
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Advanced Deep Learning Techniques for Sickle Cell Anaemia Detection
Abstract: Sickle Cell Anemia (SCA) is a prevalent genetic blood disorder characterized by the presence of abnormal hemoglobin, resulting in the distinctive sickle shape of red blood cells. Timely and accurate identification of Sickle Cell Anemia (SCA) is essential for effective management and treatment. This study presents a new method that utilizes Convolutional Neural Networks (CNNs), a deep learning model particularly effective for image analysis. The process involves using microscopic images …
Published in Research and Reviews: A Journal of Medicine · Vol. 14, Issue 3, 2024 · pp. 9–15 Read article