Image Processing Applications Research Articles

Refined Iterative Method for a Common Variational Inclusion and Common Fixed-Point Problem with Practical Applications

This paper introduces a novel parallel method for solving common variational inclusion and common fixed-point (CVI-CFP) problems. The proposed algorithm provides a strong convergence theorem established under specific conditions associated with the CVI-CFP problem. Numerical simulations demonstrate the algorithm’s efficacy in the context of signal recovery problems involving various types of blurred filters. The results highlight the algorithm’s potential for practical applications in image processing and other fields.

Design and Analysis of Optimized Approximate Multiplier Using Novel Higher-Order Compressor

The energy-efficient error-tolerant circuits have paved the way for a whole new area in low-power consumption applications with approximate computing. The approximate computing fulfills the trade-off requirement of exact computation and provides efficient performance. In this paper, a novel energy-efficient multiplier has been proposed for image processing applications. In the multiplication process, compressors are used as an important component for the reduction of partial products. Higher-order approximate 5:2 and 6:2 compressors are also designed and simulated in VIVADO using Verilog coding. The proposed higher-order compressors result in less area and low-power consumption in comparison with the existing state-of-the-art technique. These high-performance compressors are used at the multipliers’ reduction stage, resulting in an energy-efficient circuit for error-tolerant applications. All the simulations were carried out in VIVADO considering 8-bit inputs. Multiplication performance shows 37.77 % (8-bit) improvement in terms of power consumption in comparison to the conventional multiplier. The multiplication process has been done on the original, negative, and sharpened images using their masks. The proposed multiplier shows 51.36% (original image), 6.04% (negative image), and 22.44% (sharpened image) PSNR improvement in comparison to state-of-the-art work.

Improving Underwater Photogrammetric 3D Reconstruction Processing of Shipwreck Sites

Abstract. Applying image processing algorithms to enhance the clarity of underwater images can significantly assist in the visual interpretation of subsea environments. Taking advantage of techniques to maximise the information extracted from the captured input data to produce the highest quality output ensures a higher utilisation of the valuable recorded content which may have been collected at the high cost of a laborious diver survey or an expensive ROV (Remotely Operated Vehicle) expedition. Adopting photogrammetric 3D reconstruction processing in underwater imaging has enabled the creation of high-fidelity digital 3D models which have been used to map subsea infrastructure for maintenance and repair, coral reefs for ecological monitoring, and in the primary context of this article, shipwrecks sites for maritime archaeology and accident investigation. Alongside exploring algorithms to reduce the characteristic haze of underwater photography, this article illustrates the advantages of optimising the processes utilised at distinct stages of the photogrammetric 3D reconstruction workflow to improve the photorealism of the digital 3D models.

UK national survey on nuclear medicine in-house clinical software: the calm before the storm.

The use of in-house developed software as a medical device (IHD-SaMD) is core to many nuclear medicine (NM) services in the UK, including applications in nonimaging studies and image processing. Expected regulatory changes in 2025 could have significant implications due to a lack of resources and expertise in the implementation and maintenance of software Quality Management Systems (QMS) and associated standards. This survey investigated the national use of IHD-SaMD and the readiness of services to adapt to the upcoming regulatory changes. An online survey was used to investigate the current national usage of IHD-SaMD. Representatives of 64 UK NM physics services were invited to participate, with 43 responding. It was found that 98% of respondents use IHD-SaMD clinically. About 65% use IHD-SaMD that respondents felt was under-supported (e.g. legacy software). Approximately 60% of respondents use or support two or more pieces of IHD-SaMD. Around 66% of respondents use a QMS in their department, with about 48% using a software-specific QMS. Most respondents indicate understaffing, particularly with regard to IT/software skillsets. Almost all respondents indicate without an increase in the preparedness and understanding of the requirements, all dependent clinical services would be severely impacted or indeed stopped. This national survey shows that pending regulatory changes could significantly impact NM services, up to and including stopping clinical services. Additional resources would be required to support in-house software management under an appropriate QMS or move to European conformity marking (CE)-marked software where available. This must be urgently considered and addressed by all NM stakeholders.

Image Matching for Multi spectral Image Satellite SIFT and Features

Abstract The Matching and registration of the satellite imagery play an essential role in many remote sensing and image processing applications. Certain steps, such as analysis of broad regions of interest and for the remotely detection. Pixel values in two or more images were play an important role in matching and detection algorithms between two or more images. In this work an image registration process (detection, extract and matching features) were utilized through using the sift algorithm to registration two image that have different resolutions (Landsat (30m) and Sentinel (10m)) utilize MATLAB program. A geometric correction was applied in this work for detect a correct points between these images. The results shows a corresponding between these two images. A preprocessing were utilized in good identifying features and accuracy of extracting information from satellite images.

Deep convolutional neural networks for ship detection using refined DOTA and TGRS-HRRSD high-resolution image datasets

Automated identification of ships in satellite imagery is of utmost significance in many military and civilian applications, including monitoring maritime traffic and maintaining maritime security. However, ship detection from remotely sensed imagery is a challenging task because of their varying sizes, orientations, types, and various interferences. In recent years, deep learning algorithms have become a powerful tool in image processing applications, including image classification and object detection due to their strong feature representation capabilities. In this study, a refined ship dataset, containing a total of 13,735 instances and representing different ship types, was generated using state-of-the-art datasets for the ship detection task. In a comparative framework, the performance of five deep learning algorithms (Faster R-CNN, YOLOv8, CenterNet, EfficientDet, and SSD) in ship detection was investigated using the created dataset. The performance of the deep learning models was compared using MS COCO accuracy metrics. In addition, the generalization capabilities of the models were tested on an independent image acquired by Göktürk-1 having a mucilage effect that undermines the spectral discrimination. Results revealed that the YOLOv8 model had the greatest ship detection performance (AP@0.50 = 96.33 %), followed by CenterNet (AP@0.50 = 88.80 %), Faster R-CNN (AP@0.50 = 85.30 %), EfficientDet (AP@0.50 = 78.91 %) and SSD (AP@0.5 = 75.98 %), and obtained the highest accuracies in terms of other COCO metrics. In addition, generalization capabilities on the test image confirmed the superiority of the YOLOv8 model with AP@0.50 score of 63.93 %. Overall, the results of this study validated the effectiveness of the YOLOv8 model in identifying ship targets in remotely sensed images.

A Review on ML and DL Techniques in Detecting Plant Diseases

In Our country, Agriculture is the main occupation of the people. Plant diseases are a major problem today as they affect the quality and production of agricultural production. Most plant-borne diseases are caused by viruses and fungi. Identifying the diseases in early stage by manual power is not possible in large sector of Area. Subsequent maintenance of plants conditions are major challenging task. To overcome this problem in Agriculture, we use Image Processing Technique to identify the disease in beginning stage . To Apply Image Processing Technique, we have to undergo certain Image concepts that deals with image acquisition, image pre-processing, image segmentation, feature extraction and classification of disease. This Methodology reduces the destruction of plants and make the crop production high. The digital Image Processing Technique is the main solution to solve the problems in Plant diseases by identifying the plant diseases in early stage. It is very much useful to the Farmers who are facing plant diseases problem in their Agriculture Area. Many techniques are used to identify the diseases in plants using classifiers such as , K-Nearest Neighbors ,Support Vector Machine methods etc. This paper gives the overview of available methods for plant disease detection

Graphene nanoplatelet additive impact on compression and bending strength of S-2 glass face-sheet honeycomb sandwich panels: An experimental study

In this study, the three-point bending, edge-wise, and flat-wise strengths of Nomex® sandwich structures with un-doped S-2 glass fiber face-sheet (Neat/S-2/glass) and those with Graphene nanoplatelet-doped S-2 glass fiber face-sheet (GNPs/S-2/glass) were examined parametrically. In the macroscopic images obtained post-experimentation, the damage to the structures’ face layer, core, and interface with GNP (graphene nanoplatelet) additives was visualized. Morphological images depicting the damaged structures were obtained by applying image processing techniques to the macroscopic images. Finally, the fracture mechanisms of fiber/fiber and fiber/matrix interfaces within the shell layers were examined in SEM images. As a result of the study, the addition of GNPs increased the strength of the interfaces between the fiber layers and the fiber core within the face layers of S-2 glass face-sheet Nomex® sandwich structures, leading to a 40% enhancement in bending strength, a 14% improvement in core compressive strength, and a 2.85% increase in edge-wise compressive strength. Moreover, the delamination damage between the face layer and the core, which undermines the structural integrity of the sandwich structure, was not observed in GNPs/S-2/glass sandwich structures. Thus, the damage propagation throughout the sandwich structure contributed to enhancing its load-carrying capacity. The collapse extent of the sandwich structures, determined through image processing, measured 8676 mm in Neat/S-2/glass sandwiches, whereas it amounted to 10,410 mm in GNPs/S-2/glass sandwiches. The inclusion of GNPs additive augmented the collapse strength of the sandwich structure by 19.9%. Utilizing GNPs as a filler in the matrix material has been recognized as a practical approach to enhancing the mechanical strength of sandwich structures.

Analysis of methods and algorithms for image filtering and quality enhancement

This paper addresses the prevalent issue of image noise and presents methods for its mitigation. The paper describes, analyses and tests a variety of image filtering techniques, with specific reference to their use in different contexts. The filtering methods can be classified into two principal categories: linear filters, which include the Gaussian and mean filters, and non-linear filters, which comprise the median filter, the Fast Fourier Transform (FFT), the Non-Local Means (NLM) filter, and the anisotropic diffusion filter. The efficacy of each filter is mathematically described and evaluated on RGB images using the Python programming language. The study delineates the evaluation metrics and their respective advantages and disadvantages. The Root Mean Square Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR) are employed as criteria for the analysis of algorithm efficiency. Furthermore, the mean execution time for each algorithm is also monitored. The experimental data suggests that linear filters are relatively fast but produce inferior results and are best employed as preparatory measures. Non-linear filters have been demonstrated to be more robust and applicable to a variety of noise types, although it has been established that they require parameter fine-tuning. The study demonstrates that anisotropic diffusion is suitable for both manual image processing and real-time applications, offering an optimal balance between processing speed and denoised image quality. NLM is optimal for high-quality single image processing due to its superior results, despite a slower processing speed. FFT is noted for its efficiency in eliminating periodic noise. Further research will be conducted on advancing filtering techniques for different real-world scenarios and autonomous systems.

DESIGN OF IMAGE PROCESSING APPLICATION FOR FOREIGN OBJECT INSPECTION USING DRONE ON AIRCRAFT RUNWAYS

Flight safety is a crucial aspect of the aviation industry, heavily reliant on optimal runway conditions for successful takeoffs and landings. Foreign Object Debris (FOD) such as stones and potholes on the runway pose significant threats, necessitating preventive measures to avoid operational failures and aircraft damage. This research focuses on designing an image processing application for drone usage to enhance runway inspections. The Research and Development (R&D) method is employed from application design to field testing. Image processing utilizes the You Only Look Once (YOLO) method for swift and accurate FOD detection, maximizing drone effectiveness in identifying risks. Findings indicate that object size and image capture distance significantly affect detection performance. This analysis provides a basis for model optimization through improved data augmentation techniques and parameter adjustments to address challenges in detecting objects of various sizes.

Review of medical image processing using quantum-enabled algorithms

Efficient and reliable storage, analysis, and transmission of medical images are imperative for accurate diagnosis, treatment, and management of various diseases. Since quantum computing can revolutionize big data analytics by providing faster solutions and security tactics, numerous studies in this field have focused on the use of quantum and quantum-inspired algorithms to enhance the performance of traditional medical image processing approaches. This review aims to provide readers with a succinct yet adequate compendium of the advances in medical image processing combined with quantum behaviors for disease diagnosis and medical image security. Some open challenges are outlined, identifying the performance limitations of current quantum technology in their applications, while addressing the short-, medium-, and long-term development plans of this field in designing future quantum healthcare systems. We hope that this review will provide full guidance for upcoming researchers interested in this area and will stimulate further appetite of experts already active in this area aimed at the pursuit of more advanced quantum paradigms in medical image processing applications.

Far-field sub-diffraction focusing and controlled focus shaping of circularly polarized light using a dielectric phase plate

In recent years, sub-diffraction focusing has received substantial attention due to its versatility. However, achieving a flexible sub-diffraction focusing in the far field remains stimulating. Existing techniques either require complex fabrication facilities or are limited to the short focal length and high numerical aperture (NA) of the imaging system. Here, we introduce an optimization method for sub-diffraction focusing of a circularly polarized beam in the far field with a lens of large focal length. A cost-effective dielectric phase plate serves the purpose. By employing a phase plate composed of a thin layer of dielectric Si3N4, the phase of the propagating beam is modulated in the beam’s cross-section, which is divided into two regions of the opposite phase by the plate. A sub-diffraction focusing is achieved for a proper tunning between the two regions. In addition to sub-diffraction focusing, the phase plate is also capable of shaping the focus into a doughnut-shaped and a flat-top profile in the far field. This design provides a simple solution for sub-diffraction focusing and focus shaping that will find potential applications in optical imaging, optical trapping, and material processing.

SMALE: Hyperspectral Image Classification via Superpixels and Manifold Learning

As an extremely efficient preprocessing tool, superpixels have become more and more popular in various computer vision tasks. Nevertheless, there are still several drawbacks in the application of hyperspectral image (HSl) processing. Firstly, it is difficult to directly apply superpixels because of the high dimension of HSl information. Secondly, existing superpixel algorithms cannot accurately classify the HSl objects due to multi-scale feature categorization. For the processing of high-dimensional problems, we use the principle of PCA to extract three principal components from numerous bands to form three-channel images. In this paper, a novel superpixel algorithm called Seed Extend by Entropy Density (SEED) is proposed to alleviate the seed point redundancy caused by the diversified content of HSl. It also focuses on breaking the dilemma of manually setting the number of superpixels to overcome the difficulty of classification imprecision caused by multi-scale targets. Next, a space–spectrum constraint model, termed Hyperspectral Image Classification via superpixels and manifold learning (SMALE), is designed, which integrates the proposed SEED to generate a dimensionality reduction framework. By making full use of spatial context information in the process of unsupervised dimension reduction, it could effectively improve the performance of HSl classification. Experimental results show that the proposed SEED could effectively promote the classification accuracy of HSI. Meanwhile, the integrated SMALE model outperforms existing algorithms on public datasets in terms of several quantitative metrics.

Application of Image Processing for Tool Flank Wear Measurement and Optimization Using the Taguchi Method

Application of Image Processing for Tool Flank Wear Measurement and Optimization Using the Taguchi Method

Attenuated color channel adaptive correction and bilateral weight fusion for underwater image enhancement

Due to the absorption and scattering of light and the influence of suspended particles, underwater images commonly exhibit color distortions, reduced contrast, and diminished details. This paper proposes an attenuated color channel adaptive correction and bilateral weight fusion approach called WLAB to address the aforementioned degradation issues. Specifically, a novel white balance method is first applied to balance the color channel of the input image. Moreover, a local-block-based fast non-local means method is proposed to obtain a denoised version of the color-corrected image. Then, an adaptive stretching method that considers the histogram's local features to get a contrast-enhanced version of the color-corrected image. Finally, a bilateral weight fusion method is proposed to fuse the above two image versions to obtain an output image with complementary advantages. Experimental studies are conducted on three benchmark underwater image datasets and compared with ten state-of-the-art methods. The results show that WLAB has a significant advantage over the comparative methods. Notably, WLAB exhibits a degree of independence from camera settings and enhances the precision of various image processing applications, including key points and saliency detection. Additionally, it demonstrates commendable adaptability in improving low-light and foggy images.

The Application of Tsallis Entropy Based Self-Adaptive Algorithm for Multi-Threshold Image Segmentation.

Tsallis entropy has been widely used in image thresholding because of its non-extensive properties. The non-extensive parameter q contained in this entropy plays an important role in various adaptive algorithms and has been successfully applied in bi-level image thresholding. In this paper, the relationships between parameter q and pixels' long-range correlations have been further studied within multi-threshold image segmentation. It is found that the pixels' correlations are remarkable and stable for images generated by a known physical principle, such as infrared images, medical CT images, and color satellite remote sensing images. The corresponding non-extensive parameter q can be evaluated by using the self-adaptive Tsallis entropy algorithm. The results of this algorithm are compared with those of the Shannon entropy algorithm and the original Tsallis entropy algorithm in terms of quantitative image quality evaluation metrics PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). Furthermore, we observed that for image series with the same background, the q values determined by the adaptive algorithm are consistently kept in a narrow range. Therefore, similar or identical scenes during imaging would produce similar strength of long-range correlations, which provides potential applications for unsupervised image processing.

Efficient compressed storage and fast reconstruction of large binary images using chain codes

AbstractLarge binary images are used in many modern applications of image processing. For instance, they serve as inputs or target masks for training machine learning (ML) models in computer vision and image segmentation. Storing large binary images in limited memory and loading them repeatedly on demand, which is common in ML, calls for efficient image encoding and decoding mechanisms. In the paper, we propose an encoding scheme for efficient compressed storage of large binary images based on chain codes, and introduce a new single-pass algorithm for fast parallel reconstruction of raster images from the encoded representation. We use three large real-life binary masks to test the efficiency of the proposed method, which were derived from vector layers of single-class objects – a building cadaster, a woody vegetation landscape feature map, and a road network map. We show that the masks encoded by the proposed method require significantly less storage space than standard lossless compression formats. We further compared the proposed method for mask reconstruction from chain codes with a recent state-of-the-art algorithm, and achieved between $$12\%$$ 12 % and $$33\%$$ 33 % faster reconstruction on test data.

Polarization-selective terahertz absorber based on square cyclic graphene surface

Graphene-based absorbers are highly valued in terahertz technology due to their efficient performance. In this study, we theoretically propose a tunable, high sensitivity, single-band terahertz perfect absorber with polarization coherence that exhibits stable performance across a broad range of incidence angles. Simulation results show that the absorber's sensitivity to both TM and TE waves can be maintained at about 2 THz/RIU, with optimal values above 13.3 RIU−1 for TM waves and 12.9 RIU−1 for TE waves, and all Q-factors above 52. Furthermore, the impedance matching approach is utilized to explain the mechanism underlying its efficient absorption, and the electric field distribution is used to analyze the polarization coherence phenomena. We also propose a polarization-insensitive hetero-geometric structure based on the original structure, which allows for the separation of the absorption peaks by adjusting the geometrical parameters. These results show that the structure has a wide range of applications in aerospace, communication engineering, and image processing.

Affine phase retrieval of quaternion signals

Quaternion algebra H is an extension of the complex number field, which is a noncommutative associative algebra. In recent years, quaternionic Fourier analysis has interested some mathematicians due to its applications in signal analysis and image processing. This paper addresses quaternionic affine phase retrieval (QAPR) in quaternion Euclidean spaces H M , which aims to exactly recover a signal in H M from the magnitudes of its affine measurements. We introduce the concepts of QAPR and phaselift operator in H M . Then, we characterize QAPR in terms of the real Jacobian matrix, prove that 5M is the minimal measurement number for QAPR in H M , study the stability of QAPR-sequences, and use the phaselift techniques to give some sufficient conditions on QAPR for H M which provide us with a method to construct QAPR-sequences.

Higher order and optimized symmetric 2D FIR filter design and hardware architecture implementation using CSD-CSE

In this paper, an optimized and high-performance Two-Dimensional (2D) Finite Impulse Response (FIR) filter is designed and hardware architecture is implemented for image processing applications. The higher-order circular symmetric 2D FIR filter is designed using a modified McClellan Transformation called a P4 transformation. The perfect circular symmetry in the contour of the 2D FIR filter and less complexity and error are attained by this proposed P4 Transformation. Next, the designed filter coefficients are represented by the Canonical Signed Digit (CSD) number format to attain the multiplier-less design to avoid the power complexity multipliers. Further, to reduce the hardware complexity, the Common Subexpression Elimination (CSE) technique is utilized to reduce the number of adders and hence area and power are decreased. Each sub-filter corresponding to the CSD coefficient rows is realized and integrated as a 2D FIR filter using a Fully Direct-type architecture. This 2D FIR filter architecture is coded by Verilog and synthesized by Cadence tools in a 45 nm CMOS technology library. The Delay, Power Consumption (PC), and Area reports were generated by the Genus synthesis tool and compared with the state-of-the-art works. The PC, area, and delay of the proposed filter architecture are reduced to the minimum of 13.96%, 69.1%, and 1.4%, respectively when compared to the existing filter architectures. The maximum of 10.48 times and a minimum of 1.18 times of Area Delay Product (ADP), and a maximum of 10.69 times and a minimum of 1.063 times of Power Delay Product (PDP) are smaller than the existing 2D filter architectures.