School of Instrument and Electronics, North University of China, Taiyuan, Shanxi Province, People's Republic of China.
PLoS One. 2023 Feb 9;18(2):e0274054. doi: 10.1371/journal.pone.0274054. eCollection 2023.
Image contrast enhancement uses the object intensity transformation function to maximize the amount of information to enhance an image. In this paper, the image enhancement problem is regarded as an optimization problem, and the particle swarm algorithm is used to obtain the optimal solution. First, an improved particle swarm optimization algorithm is proposed. In this algorithm, individual optimization, local optimization, and global optimization are used to adjust the particle's flight direction. In local optimization, the topology is used to induce comparison and communication between particles. The sparse penalty term in speed update formula is added to adjust the sparsity of the algorithm and the size of the solution space. Second, the three channels of the color images R, G, and B are represented by a quaternion matrix, and an improved particle swarm algorithm is used to optimize the transformation parameters. Finally, contrast and brightness elements are added to the fitness function. The fitness function is used to guide the particle swarm optimization algorithm to optimize the parameters in the transformation function. This paper verifies via two experiments. First, improved particle swarm algorithm is simulated and tested. By comparing the average values of the four algorithms under the three types of 6 test functions, the average value is increased by at least 15 times in the single-peak 2 test functions: in the multi-peak and multi-peak fixed-dimension 4 test functions, this paper can always search for the global optimal solution, and the average value is either the same or at least 1.3 times higher. Second, the proposed algorithm is compared with other evolutionary algorithms to optimize contrast enhancement, select images in two different data sets, and calculate various evaluation indicators of different algorithms under different images. The optimal value is the algorithm in this paper, and the performance indicators are at least a 5% increase and a minimum 15% increase in algorithm running time. Final results show that the effects the proposed algorithm have obvious advantages in both subjective and qualitative aspects.
图像对比度增强使用目标强度变换函数来最大化信息量以增强图像。在本文中，将图像增强问题视为优化问题，并使用粒子群算法获得最优解。首先，提出了一种改进的粒子群优化算法。在该算法中，个体优化、局部优化和全局优化用于调整粒子的飞行方向。在局部优化中，使用拓扑结构来诱导粒子之间的比较和通信。在速度更新公式中添加稀疏惩罚项，以调整算法的稀疏度和解空间的大小。其次，将彩色图像 R、G 和 B 的三个通道表示为四元数矩阵，并使用改进的粒子群算法优化变换参数。最后，在适应度函数中添加对比度和亮度元素。适应度函数用于指导粒子群优化算法优化变换函数中的参数。本文通过两个实验进行了验证。首先，对改进的粒子群算法进行了模拟和测试。通过比较四种算法在六种测试函数的三种类型下的平均值，在单峰 2 测试函数中，至少将四种算法的平均值提高了 15 倍：在多峰和多峰固定维 4 测试函数中，本文始终可以搜索到全局最优解，并且平均值要么相同，要么至少高 1.3 倍。其次，将所提出的算法与其他进化算法进行了比较，以优化对比度增强，选择两个不同数据集的图像，并计算不同算法在不同图像下的各种评估指标。最优值是本文中的算法，性能指标至少增加了 5%，算法运行时间至少增加了 15%。最终结果表明，所提出的算法在主观和定性方面都具有明显的优势。
