Aiming at the problem of poor image illumination correction accuracy, a kernel extreme learning machine optimized based on the differential evolution-improved marine predators algorithm is proposed to estimate the scene illumination information and restore the image. First, in order to solve the problem of randomization of the initial population of the marine predators algorithm, differential evolutions is used to provide a set of suitable initial populations to the algorithm. Then, an improved algorithm is used to optimize the weight and bias of the kernel extreme learning machine to solve the problem of the randomness of the weight and bias, so that the learning machine converges to the global optimal value and avoids the unstable predictions. Finally, after obtaining the predicted illumination information, the image model is corrected to the image effect under standard illumination through diagonal transformation. It can be seen from the experimental results that the best predictive value of the differential evolution marine predator algorithm learning machine is 0.0135915, and the quasi-bias is only 0.001687. Compared with traditional illumination estimation algorithms such as random vector functional link and extreme learning machine, the differential evolution marine predator algorithm learning machine algorithm proposed in this article has better stability, higher accuracy of calculated predicted values, and better image restoration effect.

中文翻译：

---

基于改进海洋捕食者算法的优化核极限学习机光照校正

针对图像光照校正精度较差的问题，提出一种基于差分进化改进的海洋捕食者算法优化的核极限学习机，用于估计场景光照信息并恢复图像。首先，为了解决海洋捕食者算法的初始种群随机化问题，利用差分进化为算法提供一组合适的初始种群。然后，采用改进算法对核极限学习机的权重和偏差进行优化，解决权重和偏差的随机性问题，使学习机收敛到全局最优值，避免预测不稳定。最后，在得到预测的光照信息后，通过对角变换将图像模型校正为标准光照下的图像效果。从实验结果可以看出，差分进化海洋捕食者算法学习机的最佳预测值为0.0135915，准偏差仅为0.001687。与随机向量泛函链接、极限学习机等传统光照估计算法相比，本文提出的差分进化海洋捕食者算法学习机算法具有更好的稳定性，计算出的预测值精度更高，图像恢复效果更好。从实验结果可以看出，差分进化海洋捕食者算法学习机的最佳预测值为0.0135915，准偏差仅为0.001687。与随机向量泛函链接、极限学习机等传统光照估计算法相比，本文提出的差分进化海洋捕食者算法学习机算法具有更好的稳定性，计算出的预测值精度更高，图像恢复效果更好。从实验结果可以看出，差分进化海洋捕食者算法学习机的最佳预测值为0.0135915，准偏差仅为0.001687。与随机向量泛函链接、极限学习机等传统光照估计算法相比，本文提出的差分进化海洋捕食者算法学习机算法具有更好的稳定性，计算出的预测值精度更高，图像恢复效果更好。