Patch-based Denoising Algorithms for Single and Multi-view Images

In general, all single and multi-view digital images are captured using sensors, where they are often contaminated with noise, which is an undesired random signal. Such noise can also be produced during transmission or by lossy image compression. Reducing the noise and enhancing those images is among the fundamental digital image processing tasks. Improving the performance of image denoising methods, would greatly contribute to single or multi-view image processing techniques, e.g. segmentation, computing disparity maps, etc. Patch-based denoising methods have recently emerged as the state-of-the-art denoising approaches for various additive noise levels. This thesis proposes two patch-based denoising methods for single and multi-view images, respectively. A modification to the block matching 3D algorithm is proposed for single image denoising. An adaptive collaborative thresholding filter is proposed which consists of a classification map and a set of various thresholding levels and operators. These are exploited when the collaborative hard-thresholding step is applied. Moreover, the collaborative Wiener filtering is improved by assigning greater weight when dealing with similar patches. For the denoising of multi-view images, this thesis proposes algorithms that takes a pair of noisy images captured from two different directions at the same time (stereoscopic images). The structural, maximum difference or the singular value decomposition-based similarity metrics is utilized for identifying locations of similar search windows in the input images. The non-local means algorithm is adapted for filtering these noisy multi-view images. The performance of both methods have been evaluated both quantitatively and qualitatively through a number of experiments using the peak signal-to-noise ratio and the mean structural similarity measure. Experimental results show that the proposed algorithm for single image denoising outperforms the original block matching 3D algorithm at various noise levels. Moreover, the proposed algorithm for multi-view image denoising can effectively reduce noise and assist to estimate more accurate disparity maps at various noise levels

Efficient Multimodal Deep-Learning-Based COVID-19 Diagnostic System for Noisy and Corrupted Images

Introduction: In humanity\u27s ongoing fight against its common enemy of COVID-19, researchers have been relentless in finding efficient technologies to support mitigation, diagnosis, management, contact tracing, and ultimately vaccination. Objectives: Engineers and computer scientists have deployed the potent properties of deep learning models (DLMs) in COVID-19 detection and diagnosis. However, publicly available datasets are often adulterated during collation, transmission, or storage. Meanwhile, inadequate, and corrupted data are known to impact the learnability and efficiency of DLMs. Methods: This study focuses on enhancing previous efforts via two multimodal diagnostic systems to extract required features for COVID-19 detection using adulterated chest X-ray images. Our proposed DLM consists of a hierarchy of convolutional and pooling layers that are combined to support efficient COVID-19 detection using chest X-ray images. Additionally, a batch normalization layer is used to curtail overfitting that usually arises from the convolution and pooling (CP) layers. Results: In addition to matching the performance of standard techniques reported in the literature, our proposed diagnostic systems attain an average accuracy of 98% in the detection of normal, COVID-19, and viral pneumonia cases using corrupted and noisy images. Conclusions: Such robustness is crucial for real-world applications where data is usually unavailable, corrupted, or adulterated

Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction

Abstract Background Digital images are captured using sensors during the data acquisition phase, where they are often contaminated by noise (an undesired random signal). Such noise can also be produced during transmission or by poor-quality lossy image compression. Reducing the noise and enhancing the images are considered the central process to all other digital image processing tasks. The improvement in the performance of image denoising methods would contribute greatly on the results of other image processing techniques. Patch-based denoising methods recently have merged as the state-of-the-art denoising approaches for various additive noise levels. In this work, the use of the state-of-the-art patch-based denoising methods for additive noise reduction is investigated. Various types of image datasets are addressed to conduct this study. Methods We first explain the type of noise in digital images and discuss various image denoising approaches, with a focus on patch-based denoising methods. Then, we experimentally evaluate both quantitatively and qualitatively the patch-based denoising methods. The patch-based image denoising methods are analyzed in terms of quality and computational time. Results Despite the sophistication of patch-based image denoising approaches, most patch-based image denoising methods outperform the rest. Fast patch similarity measurements produce fast patch-based image denoising methods. Conclusion Patch-based image denoising approaches can effectively reduce noise and enhance images. Patch-based image denoising approach is the state-of-the-art image denoising approach

5G and IoT Based Reporting and Accident Detection (RAD) System to Deliver First Aid Box Using Unmanned Aerial Vehicle

Internet of Things (IoT) and 5G are enabling intelligent transportation systems (ITSs). ITSs promise to improve road safety in smart cities. Therefore, ITSs are gaining earnest devotion in the industry as well as in academics. Due to the rapid increase in population, vehicle numbers are increasing, resulting in a large number of road accidents. The majority of the time, casualties are not appropriately discovered and reported to hospitals and relatives. This lack of rapid care and first aid might result in life loss in a matter of minutes. To address all of these challenges, an intelligent system is necessary. Although several information communication technologies (ICT)-based solutions for accident detection and rescue operations have been proposed, these solutions are not compatible with all vehicles and are also costly. Therefore, we proposed a reporting and accident detection system (RAD) for a smart city that is compatible with any vehicle and less expensive. Our strategy aims to improve the transportation system at a low cost. In this context, we developed an android application that collects data related to sound, gravitational force, pressure, speed, and location of the accident from the smartphone. The value of speed helps to improve the accident detection accuracy. The collected information is further processed for accident identification. Additionally, a navigation system is designed to inform the relatives, police station, and the nearest hospital. The hospital dispatches UAV (i.e., drone with first aid box) and ambulance to the accident spot. The actual dataset from the Road Safety Open Repository is used for results generation through simulation. The proposed scheme shows promising results in terms of accuracy and response time as compared to existing techniques

Circular Intensely Orthogonal Double Cover Design of Balanced Complete Multipartite Graphs

In this paper, we generalize the orthogonal double covers (ODC) of Kn,n as follows. The circular intensely orthogonal double cover design (CIODCD) of X=Kn,n,…,n︸m is defined as a collection T={G00,G10,…,G(n−1)0}∪{G01,G11,…,G(n−1)1} of isomorphic spanning subgraphs of X such that every edge of X appears twice in the collection T,E(Gi0)∩E(Gj0)=E(Gi1)∩E(Gj1)=0,i≠jand E(Gi0)∩E(Gj1)=λ=m2,i,j∈ℤn. We define the half starters and the symmetric starters matrices as constructing methods for the CIODCD of X. Then, we introduce some results as a direct application to the construction of CIODCD of X by the symmetric starters matrices

Segmentation of Spectral Plant Images Using Generative Adversary Network Techniques

The spectral image analysis of complex analytic systems is usually performed in analytical chemistry. Signals associated with the key analytics present in an image scene are extracted during spectral image analysis. Accordingly, the first step in spectral image analysis is to segment the image in order to extract the applicable signals for analysis. In contrast, using traditional methods of image segmentation in chronometry makes it difficult to extract the relevant signals. None of the approaches incorporate contextual information present in an image scene; therefore, the classification is limited to thresholds or pixels only. An image translation pixel-to-pixel (p2p) method for segmenting spectral images using a generative adversary network (GAN) is presented in this paper. The p2p GAN forms two neuronal models. During the production and detection processes, the representation learns how to segment ethereal images precisely. For the evaluation of the results, a partial discriminate analysis of the least-squares method was used to classify the images based on thresholds and pixels. From the experimental results, it was determined that the GAN-based p2p segmentation performs the best segmentation with an overall accuracy of 0.98 ± 0.06. This result shows that image processing techniques using deep learning contribute to enhanced spectral image processing. The outcomes of this research demonstrated the effectiveness of image-processing techniques that use deep learning to enhance spectral-image processing

FCA-VBN: Fog computing-based authentication scheme for 5G-assisted vehicular blockchain network

Emerging technology known as intelligent transportation systems allows for seamless two-way communication between moving vehicles and stationary infrastructure. Therefore, core security services in a vehicular network consist of encrypting and trusting information packets for intra- and inter-vehicle systems. However, conventional reconciliation methods incur high communication costs and security risks, and channel imperfection causes important extraction disparities. For a 5G-enabled vehicular blockchain network, we suggested a novel fog computing-based authentication approach for the 5G-assisted vehicular blockchain network (FCA-VBN) scheme, which incorporated fog computing and secure authentication to address these issues. The FCA-VBN scheme employs a channel stage response instituted private key extraction technique for terminal key agreement. Using the blockchain’s immutability and memorability, the novelty of this paper is that it shows how the intelligent contract could be utilized to build and issue the link among the fog server and the node’s data through a transaction. When it comes to message authenticity and integrity, conditional privacy protection, and unlinkability, we demonstrated that the proposed FCA-VBN system is safe and secure. We also spoke about how resistant the method is to various assaults. Ultimately, we theoretically analyzed the suggested FCA-VBN scheme’s achievement on a wide range of metrics, such as computation and communication costs and power usage, to arrive at a comprehensive evaluation