Low light image enhancement based on modified Retinex optimized by fractional order gradient descent with momentum RBF neural network

Abstract

To dynamically adjust the edge preservation and smoothness of low-light images, this paper proposed a fractional order gradient descent with momentum radial basis function neural network (FOGDMRBF) to optimizing Retinex.Its convergence is proved. In order to speed up the convergence process, an adaptive learning rate is used to adjust the training process reasonably. The results verify the theoretical results of the proposed algorithm such as its monotonicity and convergence. The descending curve of error values by FOGDM is more smoother than gradient descent and gradient descent with momentum method. The influence of regularization parameter is analyzed and compared. Compared with Dark Channel Prior, Histogram Equalization, Homomorphic Filtering and Multiple Exposure Fusion, the halo and noise generated are significantly reduced with higher Peak Signal-to-Noise Ratio and Structural Similarity Index.