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"color image processing"
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Three-Color Balancing for Color Constancy Correction
This paper presents a three-color balance adjustment for color constancy correction. White balancing is a typical adjustment for color constancy in an image, but there are still lighting effects on colors other than white. Cheng et al. proposed multi-color balancing to improve the performance of white balancing by mapping multiple target colors into corresponding ground truth colors. However, there are still three problems that have not been discussed: choosing the number of target colors, selecting target colors, and minimizing error which causes computational complexity to increase. In this paper, we first discuss the number of target colors for multi-color balancing. From our observation, when the number of target colors is greater than or equal to three, the best performance of multi-color balancing in each number of target colors is almost the same regardless of the number of target colors, and it is superior to that of white balancing. Moreover, if the number of target colors is three, multi-color balancing can be performed without any error minimization. Accordingly, we propose three-color balancing. In addition, the combination of three target colors is discussed to achieve color constancy correction. In an experiment, the proposed method not only outperforms white balancing but also has almost the same performance as Cheng’s method with 24 target colors.
Journal Article
Quaternion Matrix Optimization: Motivation and Analysis
The class of quaternion matrix optimization (QMO) problems, with quaternion matrices as decision variables, has been widely used in color image processing and other engineering areas in recent years. However, optimization theory for QMO is far from adequate. The main objective of this paper is to provide necessary theoretical foundations on optimality analysis, in order to enrich the contents of optimization theory and to pave way for the design of efficient numerical algorithms as well. We achieve this goal by conducting a thorough study on the first-order and second-order (sub)differentiation of real-valued functions in quaternion matrices, with a newly introduced operation called R-product as the key tool for our calculus. Combining with the classical optimization theory, we establish the first-order and the second-order optimality analysis for QMO. Particular treatments on convex functions, the ℓ0-norm and the rank function in quaternion matrices are tailored for a sparse low rank QMO model, arising from color image denoising, to establish its optimality conditions via stationarity.
Journal Article
Multiclass Level-Set Segmentation of Rust and Coating Damages in Images of Metal Structures
This paper describes the combined detection of coating and rust damages on painted metal structures through the multiclass image segmentation technique. Our prior works were focused solely on the localization of rust damages and rust segmentation under different ambient conditions (different lighting conditions, presence of shadows, low background/object color contrast). This paper method proposes three types of damages: coating crack, coating flaking, and rust damage. Background, paint flaking, and rust damage are objects that can be separated in RGB color-space alone. For their preliminary classification SVM is used. As for paint cracks, color features are insufficient for separating it from other defect types as they overlap with the other three classes in RGB color space. For preliminary paint crack segmentation we use the valley detection approach, which analyses the shape of defects. A multiclass level-set approach with a developed penalty term is used as a framework for the advanced final damage segmentation stage. Model training and accuracy assessment are fulfilled on the created dataset, which contains input images of corresponding defects with respective ground truth data provided by the expert. A quantitative analysis of the accuracy of the proposed approach is provided. The efficiency of the approach is demonstrated on authentic images of coated surfaces.
Journal Article
A chaotic variant of the Golden Jackal Optimizer and its application for medical image segmentation
The initial segmentation phase is crucial in image processing to simplify the image representation and extract some desired features. Different methods and techniques have been proposed for image multi-level thresholding, but they are still stuck in local optima and need improvement. Recently, a metaheuristic optimization algorithm called Golden Jackal Optimizer (GJO) has been proposed as an alternative solution. The GJO has been adopted as a good solution for many optimization problems. However, the GJO attempted to solve the convergence problem to a local minimum during execution, often leading to unsatisfactory results. Most variants of GJO are based on chaotic systems due to their easy implementation and remarkable capacity to avoid being trapped in local optima. This paper proposes a Polynomial Chebychev Symmetric Chaotic-based GJO (PCSCGJO) algorithm by combining a recently developed chaotic generating function to achieve better segmentation results. This variant improves the GJO by introducing the chaotic generating function of the Chebyshev polynomials as an update process while searching for the optimal solution. Simulation results prove the effectiveness of the PCSCGJO method and its ability to deal with different medical color images. The quality of the segmented images obtained by the proposed method was compared to well-known metaheuristic algorithms using performance metrics such as PSNR, SSIM, FSIM, and MSE. Consequently, the metrics values show that the suggested technique outperforms the other methods regarding quality and accuracy.
Journal Article
Effective recognition design in 8-ball billiards vision systems for training purposes based on Xception network modified by improved Chaos African Vulture Optimizer
This research paper presents a comprehensive investigation into the utilization of color image processing technologies and deep learning algorithms in the development of a robot vision system specifically designed for 8-ball billiards. The sport of billiards, with its various games and ball arrangements, presents unique challenges for robotic vision systems. The proposed methodology addresses these challenges through two main components: object detection and ball pattern recognition. Initially, a robust algorithm is employed to detect the billiard balls using color space transformation and thresholding techniques. This is followed by determining the position of the billiard table through strategic cropping and isolation of the primary table area. The crucial phase involves the intricate task of recognizing ball patterns to differentiate between solid and striped balls. To achieve this, a modified convolutional neural network is utilized, leveraging the Xception network optimized by an innovative algorithm known as the Improved Chaos African Vulture Optimization (ICAVO) algorithm. The ICAVO algorithm enhances the Xception network's performance by efficiently exploring the solution space and avoiding local optima. The results of this study demonstrate a significant enhancement in recognition accuracy, with the Xception/ICAVO model achieving remarkable recognition rates for both solid and striped balls. This paves the way for the development of more sophisticated and efficient billiards robots. The implications of this research extend beyond 8-ball billiards, highlighting the potential for advanced robotic vision systems in various applications. The successful integration of color image processing, deep learning, and optimization algorithms shows the effectiveness of the proposed methodology. This research has far-reaching implications that go beyond just billiards. The cutting-edge robotic vision technology can be utilized for detecting and tracking objects in different sectors, transforming industrial automation and surveillance setups. By combining color image processing, deep learning, and optimization algorithms, the system proves its effectiveness and flexibility. The innovative approach sets the stage for creating advanced and productive robotic vision systems in various industries.
Journal Article
Efficient quaternion CUR method for low-rank approximation to quaternion matrix
The low-rank quaternion matrix approximation has been successfully applied in many applications involving signal processing and color image processing. However, the cost of quaternion models for generating low-rank quaternion matrix approximation is sometimes considerable due to the computation of the quaternion singular value decomposition (QSVD), which limits their application to real large-scale data. To address this deficiency, an efficient quaternion matrix CUR (QMCUR) method for low-rank approximation is suggested, which provides significant acceleration in color image processing. We first explore the QMCUR approximation method, which uses actual columns and rows of the given quaternion matrix, instead of the costly QSVD. Additionally, two different sampling strategies are used to sample the above-selected columns and rows. Then, the perturbation analysis is performed on the QMCUR approximation of noisy versions of low-rank quaternion matrices. And we also employ the proposed QMCUR method to color image recovery problem. Extensive experiments on both synthetic and real data further reveal the superiority of the proposed algorithm compared with other algorithms for getting low-rank approximation, in terms of both efficiency and accuracy.
Journal Article
Quantum color image edge detection algorithm based on Sobel operator
Edge detection is a preprocessing step in image processing that directly affects the effectiveness of subsequent image processing. However, real-time image preprocessing is challenging to achieve with the increased number and quality of images. Therefore, this paper studies a quantum color image spatial edge detection algorithm and uses the International Business Machines Quantum (IBM Q) quantum simulation platform to simulate and verify the designed quantum circuit. First, the quantum adder, quantum subtractor, quantum maximum value calculation module, and quantum threshold operation module are optimized. Then, based on these modules, a new quantum edge detection algorithm is designed. Finally, the analysis shows that compared with existing related work, the algorithm in this paper has lower circuit complexity and fewer qubits, reducing the circuit complexity from O(n2+q3) to O(q). Finally, the proposed algorithm is simulated on the IBM Q quantum simulator, which can effectively detect the edge information of quantum color images.
Journal Article
Improved quantum image weighted average filtering algorithm
Average filtering plays a vital role in image smoothing tasks. However, existing quantum image weighted average filtering methods suffer from high circuit complexity. Therefore, this paper proposes an improved quantum color image weighted average filtering algorithm and its corresponding quantum circuit. First, we improve the quantum circuit to prepare classical color images into a quantum state. Then, an improved quantum divider is developed, and a weighted average filter is constructed using basic quantum image processing modules. Next, to enhance the universality of the filter, a quantum comparator with lower circuit complexity is used to design a noise detection module for distinguishing noise from real signals. Finally, a quantum circuit for color image weighted average filtering is designed, and simulations are conducted on the IBM Quantum Experience (IBM Q) platform to verify the feasibility of our algorithm. The analysis shows that compared with existing methods, this method significantly reduces the circuit complexity and has better filtering performance.
Journal Article
A fast image dehazing method that does not introduce color artifacts
We propose a method for color dehazing with four main characteristics: it does not introduce color artifacts, it does not depend on inverting any physical equation, it is based on models of visual perception, and it is fast, potentially real time. Our method converts the original input image to the HSV color space and works in the saturation and value domains by: (1) reducing the value component via a global constrained histogram flattening; (2) modifying the saturation component in consistency with the previous reduced value; and (3) performing a local contrast enhancement in the value component. Results show that our method competes with the state-of-the-art when dealing with standard hazy images, and outperforms it when dealing with challenging haze cases. Furthermore, our method is able to dehaze a FullHD image on a GPU in 90 ms.
Journal Article