Multilevel Image Research Articles

TransCNNLoc: End-to-end pixel-level learning for 2D-to-3D pose estimation in dynamic indoor scenes

Accurate localization in GPS-denied environments has always been a core issue in computer vision and robotics research. In indoor environments, vision-based localization methods are susceptible to changes in lighting conditions, viewing angles, and environmental factors, resulting in localization failures or limited generalization capabilities. In this paper, we propose the TransCNNLoc framework, which consists of an encoding–decoding network designed to learn more robust image features for camera pose estimation. In the image feature encoding stage, CNN and Swin Transformer are integrated to construct the image feature encoding module, enabling the network to fully extract global context and local features from images. In the decoding stage, multi-level image features are decoded through cross-layer connections while computing per-pixel feature weight maps. To enhance the framework’s robustness to dynamic objects, a dynamic object recognition network is introduced to optimize the feature weights. Finally, a multi-level iterative optimization from coarse to fine levels is performed to recover six degrees of freedom camera pose. Experiments were conducted on the publicly available 7scenes dataset as well as a dataset collected under changing lighting conditions and dynamic scenes for accuracy validation and analysis. The experimental results demonstrate that the proposed TransCNNLoc framework exhibits superior adaptability to dynamic scenes and lighting changes. In the context of static environments within publicly available datasets, the localization technique introduced in this study attains a maximal precision of up to 5 centimeters, consistently achieving superior outcomes across a majority of the scenarios. Under the conditions of dynamic scenes and fluctuating illumination, this approach demonstrates an enhanced precision capability, reaching up to 3 centimeters. This represents a substantial refinement from the decimeter scale to a centimeter scale in precision, marking a significant advancement over the existing state-of-the-art (SOTA) algorithms. The open-source repository for the method proposed in this paper can be found at the following URL: github.com/Geelooo/TransCNNloc.

A crossover-based optimization algorithm for multilevel image segmentation

A crossover-based optimization algorithm for multilevel image segmentation

A two-step image segmentation based on clone selection multi-object emperor penguin optimizer for fault diagnosis of power transformer

Power transformer is one of the critical and valuableness apparatus in the secure operation of the power system. The infrared image fault diagnosis can reflect the status of the power transformer. However, how to detect the fault point of the diverse infrared images is a difficult problem. We present a two-stage multilevel threshold image segmentation method to finish the task of power transformers fault diagnosis. The multi-objective emperor penguin optimizer based on the clone selection strategy optimizes the multi-level image segmentation method for discovering the optimal threshold. The 3DOtsu and determination of optimal target number function as the fitness function and we convert them into multi-objective optimization problems. The proposed method is compared with the multi-objective optimization algorithms and novel fault diagnosis methods under the classic images and infrared images of the power transformer. The experiment results show that MOEPO/C has a good performance than others compared algorithms in Feature Similarity Index, Uniformity measure, Peak Signal‑to‑Noise Ratio, and CPU time. Especially, the proposed method obtains high accuracy in power transformer fault diagnosis.

Multi-threshold Image Segmentation based on an improved Salp Swarm Algorithm: Case study of breast cancer pathology images

Breast cancer poses a significant risk to women’s health, and it is essential to provide proper diagnostic support. Medical image processing technology is a key component of all supporting diagnostic techniques, with Image Segmentation (IS) being one of its primary steps. Among various methods, Multilevel Image Segmentation (MIS) is considered one of the most effective and straightforward approaches. Many researchers have attempted to improve the quality of image segmentation by combining different metaheuristic algorithms with MIS. However, these methods often suffer from issues such as low convergence accuracy and a proclivity for converging towards Local Optima (LO). To overcome these challenges, this study introduces an integrated approach that combines the Salp Swarm Algorithm (SSA), Slime Mould Algorithm (SMA) and Differential Evolution (DE) algorithm. In this manuscript, we introduce an innovative hybrid MIS model termed SDSSA, which leverages elements from the SSA, SMA and DE algorithms. The SDSSA model fundamentally relies on non-local means 2D histogram and 2D Kapur’s entropy. To evaluate the proposed method effectively, we compare it initially with similar algorithms using the IEEE CEC2014 benchmark functions. The SDSSA showcases enhanced convergence velocity and precision relative to similar algorithms. Furthermore, this paper proposes an excellent MIS method. Subsequently, IS experiments were conducted separately at both low and high threshold levels. The test results demonstrate that the segmentation outcomes of MIS, at both low and high threshold levels, outperform other methods. This validates SDSSA as a superior segmentation technique that provides practical assistance for future research in breast cancer pathology image processing.

Image Emotion Analysis Combining Attention Mechanism and Multi-level Correlation

The development of social network has brought a large amount of image information, and the research on image emotion has gradually attracted wide attention. The current image emotion analysis methods based on multi-level features simply splice the features at each level and then classify the emotions, which not only ignores the correlation between features at different levels, but also ignores the synergistic effect between global features and local features. Therefore, this paper proposes an emotion model (MAML) based on mixed attention and multi-level dependence of images, which uses spatial and channel attention mechanisms to extract local emotion region features of images. Bi-directional Long Short Term Memory network (BiLSTM) is used to establish correlation between multi-level image global features. The experimental results of MAML model on artphoto and abstract data sets prove the validity of MAML model.

Lensless Image Restoration Based on Multi-Stage Deep Neural Networks and Pix2pix Architecture

Lensless imaging represents a significant advancement in imaging technology, offering unique benefits over traditional optical systems due to its compact form factor, ideal for applications within the Internet of Things (IoT) ecosystem. Despite its potential, the intensive computational requirements of current lensless imaging reconstruction algorithms pose a challenge, often exceeding the resource constraints typical of IoT devices. To meet this challenge, a novel approach is introduced, merging multi-level image restoration with the pix2pix generative adversarial network architecture within the lensless imaging sphere. Building on the foundation provided by U-Net, a Multi-level Attention-based Lensless Image Restoration Network (MARN) is introduced to further augment the generator’s capabilities. In this methodology, images reconstructed through Tikhonov regularization are perceived as degraded images, forming the foundation for further refinement via the Pix2pix network. This process is enhanced by incorporating an attention-focused mechanism in the encoder--decoder structure and by implementing stage-wise supervised training within the deep convolutional network, contributing markedly to the improvement of the final image quality. Through detailed comparative evaluations, the superiority of the introduced method is affirmed, outperforming existing techniques and underscoring its suitability for addressing the computational challenges in lensless imaging within IoT environments. This method can produce excellent lensless image reconstructions when sufficient computational resources are available, and it consistently delivers optimal results across varying computational resource constraints. This algorithm enhances the applicability of lensless imaging in applications such as the Internet of Things, providing higher-quality image acquisition and processing capabilities for these domains.

An Innovative Approach for Removing Stripe Noise in Infrared Images.

The non-uniformity of infrared detectors' readout circuits can lead to stripe noise in infrared images, which affects their effective information and poses challenges for subsequent applications. Traditional denoising algorithms have limited effectiveness in maintaining effective information. This paper proposes a multi-level image decomposition method based on an improved LatLRR (MIDILatLRR). By utilizing the global low-rank structural characteristics of stripe noise, the noise and smooth information are decomposed into low-rank part images, and texture information is adaptively decomposed into several salient part images, thereby better preserving texture edge information in the image. Sparse terms are constructed according to the smoothness of the effective information in the final low-rank part of the image and the sparsity of the stripe noise direction. The modeling of stripe noise is achieved using multi-sparse constraint representation (MSCR), and the Alternating Direction Method of Multipliers (ADMM) is used for calculation. Extensive experiments demonstrated the proposed algorithm's effectiveness and compared it with state-of-the-art algorithms in subjective judgments and objective indicators. The experimental results fully demonstrate the proposed algorithm's superiority and efficacy.

Information fusion for fully automated segmentation of head and neck tumors from PET and CT images.

PET/CT images combining anatomic and metabolic data provide complementary information that can improve clinical task performance. PET image segmentation algorithms exploiting the multi-modal information available are still lacking. Our study aimed to assess the performance of PET and CT image fusion for gross tumor volume (GTV) segmentations of head and neck cancers (HNCs) utilizing conventional, deep learning (DL), and output-level voting-based fusions. The current study is based on a total of 328 histologically confirmed HNCs from six different centers. The images were automatically cropped to a 200 × 200 head and neck region box, and CT and PET images were normalized for further processing. Eighteen conventional image-level fusions were implemented. In addition, a modified U2-Net architecture as DL fusion model baseline was used. Three different input, layer, and decision-level information fusions were used. Simultaneous truth and performance level estimation (STAPLE) and majority voting to merge different segmentation outputs (from PET and image-level and network-level fusions), that is, output-level information fusion (voting-based fusions) were employed. Different networks were trained in a 2D manner with a batch size of 64. Twenty percent of the dataset with stratification concerning the centers (20% in each center) were used for final result reporting. Different standard segmentation metrics and conventional PET metrics, such as SUV, were calculated. In single modalities, PET had a reasonable performance with a Dice score of 0.77±0.09, while CT did not perform acceptably and reached a Dice score of only 0.38±0.22. Conventional fusion algorithms obtained a Dice score range of [0.76-0.81] with guided-filter-based context enhancement (GFCE) at the low-end, and anisotropic diffusion and Karhunen-Loeve transform fusion (ADF), multi-resolution singular value decomposition (MSVD), and multi-level image decomposition based on latent low-rank representation (MDLatLRR) at the high-end. All DL fusion models achieved Dice scores of 0.80. Output-level voting-based models outperformed all other models, achieving superior results with a Dice score of 0.84 for Majority_ImgFus, Majority_All, and Majority_Fast. A mean error of almost zero was achieved for all fusions using SUVpeak , SUVmean and SUVmedian . PET/CT information fusion adds significant value to segmentation tasks, considerably outperforming PET-only and CT-only methods. In addition, both conventional image-level and DL fusions achieve competitive results. Meanwhile, output-level voting-based fusion using majority voting of several algorithms results in statistically significant improvements in the segmentation of HNC.

Study On Decorative Art of Greenbelt Pastel Lotus Embossed Wuzi Dengke Bottle In Qing Dynasty

China is a country with a long history of producing ceramics, leaving many exquisite and precious ceramic artworks. The ceramic involved in this paper is one of them. From the perspective of iconology, this study studies the Qing Dynasty porcelain “Greenbelt Pastel Lotus Embossed Wuzi Dengke Bottle” (GPLEWDB) through specific image analysis, including the form characteristics and symbolic meaning, historical background, and other contents of ceramic decoration. Then, according to the historical data, analyze the formation of the ceramic decorative art of social and cultural factors. This study attempts to change the traditional research methods of Chinese art history, in the form of case analysis, ceramic decorative elements of multi-level image analysis, to achieve a more in-depth study of ceramic cases. The study on the form and symbol of decorative patterns of GPLEWDB is helpful to link ceramic art research with ancient Chinese traditional culture, analyze the relationship between them, expand the perspective of traditional research, and promote the application of interdisciplinary research in ceramic research.

Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm.

To address the problems of low accuracy and slow convergence of traditional multilevel image segmentation methods, a symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) with multi-strategy improved pelican optimization algorithm is proposed for global optimization and image segmentation tasks. First, Sine chaotic mapping is used to improve the quality and distribution uniformity of the initial population. A spiral search mechanism incorporating a sine cosine optimization algorithm improves the algorithm's search diversity, local pioneering ability, and convergence accuracy. A levy flight strategy further improves the algorithm's ability to jump out of local minima. In this paper, 12 benchmark test functions and 8 other newer swarm intelligence algorithms are compared in terms of convergence speed and convergence accuracy to evaluate the performance of the MSIPOA algorithm. By non-parametric statistical analysis, MSIPOA shows a greater superiority over other optimization algorithms. The MSIPOA algorithm is then experimented with symmetric cross-entropy multilevel threshold image segmentation, and eight images from BSDS300 are selected as the test set to evaluate MSIPOA. According to different performance metrics and Fridman test, MSIPOA algorithm outperforms similar algorithms in global optimization and image segmentation, and the symmetric cross entropy of MSIPOA algorithm for multilevel thresholding image segmentation method can be effectively applied to multilevel thresholding image segmentation tasks.

Landscape changes in the Tombstone Territorial Park region (central Yukon, Canada) from multilevel remote sensing analysis

Northern ecosystems are vulnerable to surface disturbances caused by warming climate and human activities. The Ogilvie Mountains in central Yukon are an important region that should be monitored, as it is a major transportation corridor connecting northern communities. This study aims to characterize 35 years of landscape changes in the Tombstone Territorial Park and the surrounding region of the Ogilvie Mountains by detecting and interpreting landscape changes using a Landsat-derived trend analysis and multilevel image interpretation. Statistically significant spectral changes occurred in 24% of the study area between 1986 and 2021, and most of these changes are gradual and associated with vegetation succession and hydrological processes (i.e., erosion and deposition). Other landscape changes included wildfires, slumps, changes to riverbanks and lake shores, earlier melting of icings in the summer, degradation on the peripheries of some ice-wedge polygonal terrain, and potential insect damage to forests. Our investigation reveals that the extent and magnitude of landscape changes in the study area are influenced by climate, geomorphic setting, ecological succession, and glacial history of the region. Given a varied combination of ecotypes, spatial and temporal variations are to be expected in terms of the response of the landscape to climate and anthropogenic disturbances.

Multilevel Colonoscopy Histopathology Image Segmentation Using Particle Swarm Optimization Techniques.

Histopathology image segmentation is a challenging task in medical image processing. This work aims to segment lesion regions from colonoscopy histopathology images. Initially, the images are preprocessed and then segmented using the multilevel image thresholding technique. Multilevel thresholding is considered an optimization problem. Particle swarm optimization (PSO) and its variants, darwinian particle swarm optimization (DPSO), and fractional order darwinian particle swarm optimization (FODPSO) are used to solve the optimization problem and they generate the threshold values. The threshold values obtained are used to segment the lesion regions from the images of the colonoscopy tissue data set. Segmented images containing the lesion regions are then postprocessed to remove unnecessary regions. Experimental results reveal that the FODPSO algorithm with Otsu's discriminant criterion as the objective function achieves the best accuracy, Dice and Jaccard values of 0.89, 0.68 and 0.52, respectively, for the colonoscopy data set. The FODPSO algorithm also outperforms other optimization methods such as artificial bee colony and the firefly algorithms in terms of the accuracy, Dice and Jaccard values.

An efficient multilevel image thresholding method based on improved heap-based optimizer

Image segmentation is the process of separating pixels of an image into multiple classes, enabling the analysis of objects in the image. Multilevel thresholding (MTH) is a method used to perform this task, and the problem is to obtain an optimal threshold that properly segments each image. Methods such as the Kapur entropy or the Otsu method, which can be used as objective functions to determine the optimal threshold, are efficient in determining the best threshold for bi-level thresholding; however, they are not effective for MTH due to their high computational cost. This paper integrates an efficient method for MTH image segmentation called the heap-based optimizer (HBO) with opposition-based learning termed improved heap-based optimizer (IHBO) to solve the problem of high computational cost for MTH and overcome the weaknesses of the original HBO. The IHBO was proposed to improve the convergence rate and local search efficiency of search agents of the basic HBO, the IHBO is applied to solve the problem of MTH using the Otsu and Kapur methods as objective functions. The performance of the IHBO-based method was evaluated on the CEC’2020 test suite and compared against seven well-known metaheuristic algorithms including the basic HBO, salp swarm algorithm, moth flame optimization, gray wolf optimization, sine cosine algorithm, harmony search optimization, and electromagnetism optimization. The experimental results revealed that the proposed IHBO algorithm outperformed the counterparts in terms of the fitness values as well as other performance indicators, such as the structural similarity index (SSIM), feature similarity index (FSIM), peak signal-to-noise ratio. Therefore, the IHBO algorithm was found to be superior to other segmentation methods for MTH image segmentation.

A Multilevel Image Thresholding Based on Hybrid Jaya Algorithm and Simulated Annealing

A Multilevel Image Thresholding Based on Hybrid Jaya Algorithm and Simulated Annealing

Multilevel image thresholding based on Renyi’s entropy and golden sinus algorithm II

Multilevel image thresholding based on Renyi’s entropy and golden sinus algorithm II

An improved opposition-based Runge Kutta optimizer for multilevel image thresholding

An improved opposition-based Runge Kutta optimizer for multilevel image thresholding

How effective are current population-based metaheuristic algorithms for variance-based multi-level image thresholding?

Multi-level image thresholding is a common approach to image segmentation where an image is divided into several regions based on its histogram. Otsu’s method is the most popular method for this purpose, and is based on seeking for threshold values that maximise the between-class variance. This requires an exhaustive search to find the optimal set of threshold values, making image thresholding a time-consuming process. This is especially the case with increasing numbers of thresholds since, due to the curse of dimensionality, the search space enlarges exponentially with the number of thresholds.Population-based metaheuristic algorithms are efficient and effective problem-independent methods to tackle hard optimisation problems. Over the years, a variety of such algorithms, often based on bio-inspired paradigms, have been proposed. In this paper, we formulate multi-level image thresholding as an optimisation problem and perform an extensive evaluation of 23 population-based metaheuristics, including both state-of-the-art and recently introduced algorithms, for this purpose. We benchmark the algorithms on a set of commonly used images and based on various measures, including objective function value, peak signal-to-noise ratio, feature similarity index, and structural similarity index. In addition, we carry out a stability analysis as well as a statistical analysis to judge if there are significant differences between algorithms. Our experimental results indicate that recently introduced algorithms do not necessarily achieve acceptable performance in multi-level image thresholding, while some established algorithms are demonstrated to work better.

A novel multi-level image segmentation algorithm via random opposition learning-based Aquila optimizer

Aquila optimizer (AO) is an efficient meta-heuristic optimization method, which mimics the hunting style of Aquila in nature. However, the AO algorithm may suffer from immature convergence during the exploitation stage. In this paper, two strategies are elegantly employed into conventional AO, such as random opposition-based learning and nonlinear flexible jumping factor, which can efficiently enhance the performance of conventional AO. Experiments on [Formula: see text] benchmark functions and image segmentation demonstrate the effectiveness of the proposed algorithm. Comparison with several state-of-the-art meta-heuristic optimization techniques indicates the efficacy of the developed method.

No-Reference Image Quality Assessment of Magnetic Resonance images with multi-level and multi-model representations based on fusion of deep architectures

Accurate quality assessment of Magnetic Resonance (MR) images is essential for effective medical diagnostics, as it impacts the time spent on image acquisition and image interpretation by radiologists. Therefore, this study aims to provide a novel deep learning-based No-Reference (NR) MR Image Quality Assessment (IQA) method for the quality prediction of MR images. Therefore, in this work, an internal fusion of two complementary deep learning architectures is introduced that offers MR-specific quality-aware features. Apart from obtaining multi-level image representations of joint networks, to further enhance the quality prediction performance, features from re-trained single deep learning architectures are also used. Finally, as one of the main findings of this study, the quality assessment is performed by a high-level quality model trained on scores of quality models obtained for layers of the networks. The superiority of the method against the state-of-the-art techniques is verified by using two publicly available MRIQA benchmarks containing MR images and subjective scores provided by a large number of radiologists. As reported, the method offers superior IQA of MR images as the obtained scores are highly correlated with subjective opinions of medical specialists. It is characterized by the weighted average values of the Spearman Rank-order Correlation Coefficient, Kendall Rank-order Correlation Coefficient, and Pearson Linear Correlation Coefficient of 0.8754, 0.7185, and 0.9062, respectively.

An efficient image segmentation method based on expectation maximization and Salp swarm algorithm.

Multilevel image thresholding using Expectation Maximization (EM) is an efficient method for image segmentation. However, it has two weaknesses: 1) EM is a greedy algorithm and cannot jump out of local optima. 2) it cannot guarantee the number of required classes while estimating the histogram by Gaussian Mixture Models (GMM). in this paper, to overcome these shortages, a novel thresholding approach by combining EM and Salp Swarm Algorithm (SSA) is developed. SSA suggests potential points to the EM algorithm to fly to a better position. Moreover, a new mechanism is considered to maintain the number of desired clusters. Twenty-four medical test images are selected and examined by standard metrics such as PSNR and FSIM. The proposed method is compared with the traditional EM algorithm, and an average improvement of 5.27% in PSNR values and 2.01% in FSIM values were recorded. Also, the proposed approach is compared with four existing segmentation techniques by using CT scan images that Qatar University has collected. Experimental results depict that the proposed method obtains the first rank in terms of PSNR and the second rank in terms of FSIM. It has been observed that the proposed technique performs better performance in the segmentation result compared to other considered state-of-the-art methods.