Feature Similarity Index Research Articles

Modified salp swarm algorithm based multilevel thresholding for color image segmentation.

This paper proposes a multi-threshold image segmentation method based on modified salp swarm algorithm (SSA). Multi-threshold image segmentation method has good segmentation effect, but the segmentation precision will be affected with the increase of threshold number. To avoid the above problem, the slap swarm optimization algorithm (SSA) is presented to choose the optimal parameters of the fitting function and we use levy flight to improve the SSA. The solutions are assessed using the Kapur's entropy, Otsu and Renyi entropy fitness function during the optimization operation. The performance of the proposed algorithm is evaluated with several reference images and compared with different group algorithms. The results have been analyzed based on the best fitness values, peak signal to noise ratio (PSNR), and feature similarity index measures (FSIM). The experimental results show that the proposed algorithm outperformed other swarm algorithms.

An efficient optimal multilevel image thresholding with electromagnetism-like mechanism

Segmentation process is considered a major part of various image-processing applications due to its extreme inspiration on the subsequent image analysis. Thresholding is one of the simplest techniques for segmentation. In this paper, Renyi’s entropy is combined with electromagnetism-like mechanism optimization (EMO) to perform multilevel thresholding based color image segmentation. For statistical independent subsystems, Renyi’s entropy shows an extensive property and is applied to find best threshold value for image segmentation. The entropic parameter α can handle the additive information that is existent in the image. The feasibility of the EMO-Renyi’s based approach has been tested on various satellite and standard color images with bat algorithm (BAT), backtracking search algorithm (BSA), firefly algorithm (FA), particle swarm optimization (PSO), and wind driven optimization (WDO) for solving the multilevel color image thresholding problem. The analysis based on statistics of different optimization algorithms indicates the proposed EMO-Renyi’s algorithm to be more robust and precise for multilevel color image segmentation problem. These claims have been confirmed by comparing fidelity parameters such as mean error (ME), mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM) and entropy. Experiments on standard daily-life color images are conducted to prove the effectiveness of the proposed scheme. The results show that the proposed method can produce more promising segmentation results from the aspect of objective and subjective observations.

Comparison of Objective Image Quality Metrics to Expert Radiologists’ Scoring of Diagnostic Quality of MR Images

Image quality metrics (IQMs) such as root mean square error (RMSE) and structural similarity index (SSIM) are commonly used in the evaluation and optimization of accelerated magnetic resonance imaging (MRI) acquisition and reconstruction strategies. However, it is unknown how well these indices relate to a radiologist's perception of diagnostic image quality. In this study, we compare the image quality scores of five radiologists with the RMSE, SSIM, and other potentially useful IQMs: peak signal to noise ratio (PSNR) multi-scale SSIM (MSSSIM), information-weighted SSIM (IWSSIM), gradient magnitude similarity deviation (GMSD), feature similarity index (FSIM), high dynamic range visible difference predictor (HDRVDP), noise quality metric (NQM), and visual information fidelity (VIF). The comparison uses a database of MR images of the brain and abdomen that have been retrospectively degraded by noise, blurring, undersampling, motion, and wavelet compression for a total of 414 degraded images. A total of 1017 subjective scores were assigned by five radiologists. IQM performance was measured via the Spearman rank order correlation coefficient (SROCC) and statistically significant differences in the residuals of the IQM scores and radiologists' scores were tested. When considering SROCC calculated from combining scores from all radiologists across all image types, RMSE and SSIM had lower SROCC than six of the other IQMs included in the study (VIF, FSIM, NQM, GMSD, IWSSIM, and HDRVDP). In no case did SSIM have a higher SROCC or significantly smaller residuals than RMSE. These results should be considered when choosing an IQM in future imaging studies.

Video super resolution using non-linear regression and deep learning

ABSTRACTIn this paper, a hybrid model is developed by integrating a non-linear regression model and optimization-driven deep learner for video super resolution. Initially, the low-resolution frames are subjected to framing, and each frame is provided to both Fractional-Group Search Optimizer-based Deep Belief Network (FrGSO-DBN) classifier and the nonlinear regression model. Then, the output frames of both methods are averaged by the proposed hybrid model and the enhanced video frame is generated. Here, the FrGSO is developed by modifying the update process of the GSO using the fractional theory, to train the DBN such that the weights in the DBN are selected optimally. Finally, the simulation results reveal that the proposed hybrid model achieves high values of 0.8697, 25.637 dB, 0.9171, and 49.821 dB, for the metrics Structural Similarity Index Measure, Peak Signal to Noise Ratio, Feature Similarity Index Measure, and Second Derivative like Measurement of Enhancement, respectively.

Fuzzy Based Combinatorial Filter Model for Drusen Enhancement in Retinal Fundus Images

This article presents an approach to enhance drusen regions in retinal fundus image of a patient having Age-related Macular Degeneration (AMD). In this approach, a new filter model is developed by combining two processes on images which is named as Combinatorial Filter Model (CFM). The first process is based on processing drusen significant image bit planes and the second process is based on implementing fuzzy inference system (FIS) on bit planes. When bit planes are independently processed, the result improves the visibility of drusen features. An FIS is constructed to process the bit planes to further enhance the processed image. This approach is tested on images with drusen features on good quality images in proprietary database and comparatively low quality images from STARE database. The objective study of this model shows that drusen elements are enhanced and validated to a 95% significant level. The quality of the enhanced image is evaluated for preservation of drusen features using a proven feature similarity index technique with 0.99 quality index values

A context sensitive energy thresholding based 3D Otsu function for image segmentation using human learning optimization

In this paper, a novel context-based 3D Otsu algorithm using human learning optimization (HLO) is proposed for multilevel color image segmentation. The performance of 3D Otsu algorithm is reported to be poor while dealing with between-class variances through the aid of three-dimensional histogram. To overcome this problem, the concept of context thresholding has been exploited to derive pixel intensity values and spatial information. The nature of spatial context and histogram of an image is very similar. The use of energy curve for 3D Otsu gives satisfactory results but it is more time-consuming during the process of threshold selection. HLO is a recently developed meta-heuristic optimization algorithm that involves the use of learning operators developed by mimicking human learning mechanisms. In this paper, in order to avoid an exhaustive search to obtain optimal thresholds, HLO is used. Experimental studies reported in this paper demonstrate that the proposed method is better than the histogram-based 1D Otsu, 2D Otsu, and 3D Otsu methods. These claims have been confirmed by comparing fidelity parameters such as mean error (ME), mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM) and entropy.

Oppositional symbiotic organisms search optimization for multilevel thresholding of color image

Selection of optimal threshold is the most crucial issue in threshold-based segmentation. In case of color image, this task is become challenging, because conventional color image segmentation has computational complexity and also it suffers from lack of accuracy. Various techniques such as threshold based, region growing, edge detection, graph cut, pixel classification, neural network, active contour, gray level co-occurrence matrix are proposed so far for image segmentation in the literature. Out of them, threshold-based segmentation is popular for its simplicity. To address the problem of color image segmentation, we propose an enhanced version of metaheuristic optimization algorithm called Opposition based Symbiotic Organisms Search (OSOS) to solve multilevel image thresholding technique for color image segmentation by introducing opposition based learning concepts to accelerate the convergence rate and enhance the performance of standard symbiotic organisms search (SOS). The performance of the proposed OSOS based algorithm is investigated thoroughly and compared with some existing techniques like Cuckoo Search (CS), BAT algorithm (BAT), artificial bee colony (ABC) and particle swarm optimization (PSO). The comparison is made by applying the algorithm to a set of color images taken from a well-known benchmark dataset (Berkeley Segmentation Dataset (BSDS)) and some of the color images collected for the COCO dataset. It is observed from the results that the performance of the OSOS based algorithm is promising with respect to standards SOS and others in terms of the values of objective functions as well as the values of some well-defined quality metrics such as peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and feature similarity index (FSIM). The results of the proposed algorithm may encourage the scientists and engineers to apply it into pattern recognition problems.

Dragonfly Algorithm with Opposition-Based Learning for Multilevel Thresholding Color Image Segmentation

Multilevel thresholding is a very active research field in image segmentation, and has been successfully used in various applications. However, the computational time will increase exponentially as the number of thresholds increases, and for color images which contain more information this is even worse. To overcome the drawback while maintaining segmentation accuracy, a modified version of dragonfly algorithm (DA) with opposition-based learning (OBLDA) for color image segmentation is proposed in this paper. The opposition-based learning (OBL) strategy simultaneously considers the current solution and the opposite solution, which are symmetrical in the search space. With the introduction of OBL, the proposed algorithm has a faster convergence speed and more balanced exploration–exploitation compared with the original DA. In order to clearly demonstrate the outstanding performance of the OBLDA, the proposed method is compared with seven state-of-the-art meta-heuristic algorithms, through experiments on 10 test images. The optimal threshold values are calculated by the maximization of between-class variance and Kapur’s entropy. Meanwhile, some indicators, including peak signal to noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM), the average fitness values, standard deviation (STD), and computation time are used as evaluation criteria in the experiments. The promising results reveal that proposed method has the advantages of high accuracy and remarkable stability. Wilcoxon’s rank sum test and Friedman test are also performed to verify the superiority of OBLDA in a statistical way. Furthermore, various satellite images are also included for robustness testing. In conclusion, the OBLDA algorithm is a feasible and effective method for multilevel thresholding color image segmentation.

Oppositional elephant herding optimization with dynamic Cauchy mutation for multilevel image thresholding

This paper presents an improved elephant herding optimization (IEHO) to solve the multilevel image thresholding problem for image segmentation by introducing oppositional-based learning (OBL) and dynamic cauchy mutation (DCM). OBL accelerates the convergence rate and enhances the performance of standard EHO whereas DCM mitigates the premature convergence. The suggested optimization approach maximizes two popular objective functions: ‘Kapur’s entropy’ and ‘between-class variance’ to estimate optimized threshold values for segmentation of the image. The performance of the proposed technique is verified on a set of test images taken from the benchmark Berkeley segmentation dataset. The results are analyzed and compared with conventional EHO and other four popular recent metaheuristic algorithms namely cuckoo search, artificial bee colony, bat algorithm, particle swarm optimization and one classical method named dynamic programming found from the literature. Experimental results show that the proposed IEHO provides promising performance compared to other methods in view of optimized fitness value, peak signal-to-noise ratio, structure similarity index and feature similarity index. The suggested algorithm also has better convergence than the other methods taken into consideration.

Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation

An efficient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as GOA–jDE, and it merges GOA with self-adaptive differential evolution (jDE) to improve the search efficiency, preserving the population diversity especially in the later iterations. A series of experiments is conducted on various satellite images for evaluating the performance of the algorithm. Both low and high levels of the segmentation are taken into account, increasing the dimensionality of the problem. The proposed approach is compared with the standard color image thresholding methods, as well as the advanced satellite image thresholding techniques based on different criteria. Friedman test and Wilcoxon’s rank sum test are performed to assess the significant difference between the algorithms. The superiority of the proposed method is illustrated from different aspects, such as average fitness function value, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), standard deviation (STD), convergence performance, and computation time. Furthermore, natural images from the Berkeley segmentation dataset are also used to validate the strong robustness of the proposed method.

Masi Entropy for Satellite Color Image Segmentation Using Tournament-Based Lévy Multiverse Optimization Algorithm

A novel multilevel threshold segmentation method for color satellite images based on Masi entropy is proposed in this paper. Lévy multiverse optimization algorithm (LMVO) has a strong advantage over the traditional multiverse optimization algorithm (MVO) in finding the optimal solution for the segmentation in the three channels of an RGB image. As the work advancement introduces a Lévy multiverse optimization algorithm which uses tournament selection instead of roulette wheel selection, and updates some formulas in the algorithm with mutation factor. Then, the proposal is called TLMVO, and another advantage is that the population diversity of the algorithm in the latest iterations is maintained. The Masi entropy is used as an application and combined with the improved TLMVO algorithm for satellite color image segmentation. Masi entropy combines the additivity of Renyi entropy and the non-extensibility of Tsallis entropy. By increasing the number of thesholds, the quality of segmenttion becomes better, then the dimensionality of the problem also increases. Fitness function value, average CPU running time, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) were used to evaluate the segmentation results. Further statistical evaluation was given by Wilcoxon's rank sum test and Friedman test. The experimental results show that the TLMVO algorithm has wide adaptability to high-dimensional optimization problems, and has obvious advantages in objective function value, image quality detection, convergence performance and robustness.

Human mental search-based multilevel thresholding for image segmentation

Multilevel thresholding is one of the principal methods of image segmentation. These methods enjoy image histogram for segmentation. The quality of segmentation depends on the value of the selected thresholds. Since an exhaustive search is made for finding the optimum value of the objective function, the conventional methods of multilevel thresholding are time-consuming computationally, especially when the number of thresholds increases. Use of evolutionary algorithms has attracted a lot of attention under such circumstances. Human mental search algorithm is a population-based evolutionary algorithm inspired by the manner of human mental search in online auctions. This algorithm has three interesting operators: (1) clustering for finding the promising areas, (2) mental search for exploring the surrounding of every solution using Levy distribution, and (3) moving the solutions toward the promising area. In the present study, multilevel thresholding is proposed for image segmentation using human mental search algorithm. Kapur (entropy) and Otsu (between-class variance) criteria were used for this purpose. The advantages of the proposed method are described using twelve images and in comparison with other existing approaches, including genetic algorithm, particle swarm optimization, differential evolution, firefly algorithm, bat algorithm, gravitational search algorithm, and teaching-learning-based optimization. The obtained results indicated that the proposed method is highly efficient in multilevel image thresholding in terms of objective function value, peak signal to noise, structural similarity index, feature similarity index, and the curse of dimensionality. In addition, two nonparametric statistical tests verified the efficiency of the proposed algorithm, statistically.

Kapur's Entropy for Color Image Segmentation Based on a Hybrid Whale Optimization Algorithm.

In this paper, a new hybrid whale optimization algorithm (WOA) called WOA-DE is proposed to better balance the exploitation and exploration phases of optimization. Differential evolution (DE) is adopted as a local search strategy with the purpose of enhancing exploitation capability. The WOA-DE algorithm is then utilized to solve the problem of multilevel color image segmentation that can be considered as a challenging optimization task. Kapur’s entropy is used to obtain an efficient image segmentation method. In order to evaluate the performance of proposed algorithm, different images are selected for experiments, including natural images, satellite images and magnetic resonance (MR) images. The experimental results are compared with state-of-the-art meta-heuristic algorithms as well as conventional approaches. Several performance measures have been used such as average fitness values, standard deviation (STD), peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), Wilcoxon’s rank sum test, and Friedman test. The experimental results indicate that the WOA-DE algorithm is superior to the other meta-heuristic algorithms. In addition, to show the effectiveness of the proposed technique, the Otsu method is used for comparison.

A Novel Hybrid Harris Hawks Optimization for Color Image Multilevel Thresholding Segmentation

Multilevel thresholding has got more attention in recent years with various successful applications. However, the implementation becomes more and more complex and time-consuming when the number of thresholds is high, and color images which contain more information are even worse. Therefore, this paper proposes an alternative hybrid algorithm for color image segmentation, the advantages of which lie in extracting the best features from the high performance of two algorithms and overcoming the limitations of each algorithm to some extent. Two techniques, Otsu's method, and Kapur's entropy, are used as fitness function to determine the segmentation threshold values. Harris hawks optimization (HHO) is a novel and general-purpose algorithm, and the hybridization of HHO is fulfilled by adding another powerful algorithm-differential evolution (DE), which is known as HHO-DE. More specifically, the whole population is divided into two equal subpopulations which will be assigned to HHO and DE algorithms, respectively. Then both algorithms operate in parallel to update the positions of each subpopulation during the iterative process. In order to fully demonstrate the superior performance of HHO-DE, the proposed method is compared with the seven state-of-the-art algorithms by an array of experiments on ten benchmark images. Meanwhile, five measures, including the average fitness values, standard deviation (STD), peak signal to noise ratio (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM), are used to evaluate the performance of each algorithm. In addition, Wilcoxon's rank sum test for statistical analysis and the comparison with the super-pixel method are also conducted to verify the superiority of HHO-DE. The experimental results reveal that the proposed method significantly outperforms other algorithms. Hence, the HHO-DE algorithm is a remarkable and promising tool for multilevel thresholding color image segmentation.

Modified dragonfly algorithm based multilevel thresholding method for color images segmentation.

Accurate image segmentation is the preprocessing step of image processing. Multi-level threshold segmentation has important research value in image segmentation, which can effectively solve the problem of region analysis of complex images, but the computational complexity increases accordingly. In order to overcome this problem, an modified Dragonfly algorithm (MDA) is proposed to determine the optimal combination of different levels of thresholds for color images. Chaotic mapping and elite opposition-based learning strategies (EOBL) are used to improve the randomness of the initial population. The hybrid algorithm of Dragonfly Algorithms (DA) and Differential Evolution (DE) is used to balance the two basic stages of optimization: exploration and development. Kapur entropy, minimum cross-entropy and Otsu method are used as fitness functions of image segmentation. The performance of 10 test color images is evaluated and compared with 9 different meta-heuristic algorithms. The results show that the color image segmentation method based on MDA is more effective and accurate than other competitors in average fitness value (AF), standard deviation (STD), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and feature similarity index (FSIM). Friedman test and Wilcoxon's rank sum test are also performed to assess the significant difference between the algorithms.

Hybrid Multiverse Optimization Algorithm With Gravitational Search Algorithm for Multithreshold Color Image Segmentation

Multithreshold segmentation of color images is an important part of modern image processing. Multithreshold segmentation techniques that use the ideas of optimization in mathematics to process the information contained in the three channels of RGB images have received much attention in recent years. In this paper, a hybrid algorithm of multiverse optimization algorithm with a gravitational search algorithm (GSMVO) is proposed. On the one hand, the gravitational mechanism among individuals in the universe enables particles to share information and move toward particles with large mass (the optimal solution), thus optimizing local optimization. On the other hand, the problem of a slow interpopulation update is improved. On the premise of improving the optimization precision, the convergence speed of the whole system is accelerated and the robustness can be improved. The time complexity of a proposed hybrid algorithm is analyzed. The quality of a segmented image is evaluated by using peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), and Wilcoxon test. The experimental results illustrate that the proposed method has obvious advantages in objective function value, image quality measurement, convergence performance, and robustness.

Weighted lp Norm Sparse Error Constraint Based ADMM for Image Denoising

In the process of image denoising, the accurate prior knowledge cannot be learned due to the influence of noise. Therefore, it is difficult to obtain better sparse coefficients. Based on this consideration, a weighted lp norm sparse error constraint (WPNSEC) model is proposed. Firstly, the suitable setting of power p in the lp norm is made a detailed analysis. Secondly, the proposed model is extended to color image denoising. Since the noise of RGB channels has different intensities, a weight matrix is introduced to measure the noise levels of different channels, and a multichannel weighted lp norm sparse error constraint algorithm is proposed. Thirdly, in order to ensure that the proposed algorithm is tractable, the multichannel WPNSEC model is converted into an equality constraint problem solved via alternating direction method of multipliers (ADMM) algorithm. Experimental results on gray image and color image datasets show that the proposed algorithms not only have higher peak signal‐to‐noise ratio (PSNR) and feature similarity index (FSIM) but also produce better visual quality than competing image denoising algorithms.

A Novel Method for Multilevel Color Image Segmentation Based on Dragonfly Algorithm and Differential Evolution

Multilevel thresholding is a simple and powerful image segmentation method that has received widespread attention in recent years. However, the accuracy and stability of thresholding techniques are still not ideal and cannot meet the needs of engineering problems. Therefore, this paper presents a memetic algorithm of dragonfly algorithm (DA) and differential evolution (DE) for color image segmentation, which is known as improved DA (IDA). On the one hand, DA algorithm has a satisfied capability of avoiding convergence to the local optimum, thus it is served as a global search technique. On the other hand, the DE algorithm is adopted as a local search technique, which can increase the precision of solutions. In this paper, two thresholding techniques, namely, Otsu and minimum cross entropy (MCE) methods are used to determine the optimal threshold values. In order to evaluate the performance of the proposed method, we conduct a series of experiments on color images from the Berkeley database and the results are compared with five state-of-the-art meta-heuristic algorithms. Besides, a non-parametric Wilcoxon's rank sum test is also included for statistical analysis. From the experimental results, it is found that IDA-based method outperforms other compared methods in terms of average fitness values, standard deviation, peak signal to noise ratio, structural similarity index, and feature similarity index. The promising results indicate that the application of IDA-based thresholding technique is potential and meaningful.

Multiverse Optimization Algorithm Based on Lévy Flight Improvement for Multithreshold Color Image Segmentation

Multithreshold segmentation is an indispensable part of modern image processing. Color images contain more information than gray images, therefore RGB multi-thresholding segmentation techniques have been drawn much attention during recent years. Multiverse optimization (MVO) algorithm has a strong advantage in finding the optimal solution of three channels for RGB. In this paper, an MVO algorithm based on Levy flight (LMVO) is proposed. Levy flight is an efficient strategy which can not only increase the population diversity to prevent premature convergence but also improve the ability to jump out of the local optimum. Therefore, LMVO conduces to achieve a better balance between exploration and exploitation of MVO, so that it is faster and more robust than MVO and avoids premature convergence. Further LMVO algorithm is compared with the other eight famous meta-heuristics algorithms, by maximizing the objective function of Kapur's entropy method or of Otsu method to determine the optimal threshold. The maximum objective function, peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), CPU calculation time, optimal threshold value, and Wilcoxon's rank-sum test are used to evaluate the quality of the segmented image. The experimental results show that this method has obvious advantages in terms of objective function value, image quality measurement, convergence performance, and robustness.

Multi-Strategy Emperor Penguin Optimizer for RGB Histogram-Based Color Satellite Image Segmentation Using Masi Entropy

In order to realize the multilevel thresholding segmentation of color satellite images, a multi-strategy emperor penguin optimizer (called MSEPO) is proposed to find the optimal threshold values for three channels of RGB images. Masi entropy is utilized as the objective function. Meanwhile, three strategies are introduced, namely highly disruptive polynomial mutation, Levy flight, and thermal exchange operator. Through these, the MSEPO is able to properly balance the exploration and exploitation mechanisms. Moreover, the convergence, accuracy and stability performance have been significantly enhanced. Tests are carried out on color Berkeley images and color satellite images at various threshold levels. The experimental results show that the proposed method achieves higher Peak Signal to Noise Ratio (PSNR), higher Structural Similarity Index (SSIM), higher Feature Similarity Index (FSIM), and shorter CPU time than seven state-of-the-art optimization techniques. To present in a comprehensive manner, the computational complexity has also been analyzed in terms of time and space complexity. Wilcoxon rank sum test and Friedman test are also applied to statistical analysis. To sum up, MSEPO algorithm has achieved significant improvement and superior performance. What's more, the proposed technique is more suitable for high-dimensional segmentation of complex satellite images.