A Nonlinear Gradient Domain‐Guided Filter Optimized by Fractional‐Order Gradient Descent with Momentum RBF Neural Network for Ship Image Dehazing

Abstract

To avoid the blurred edges, noise, and halos caused by guided image filtering algorithm, this paper proposed a nonlinear gradient domain‐guided image filtering algorithm for image dehazing. To dynamically adjust the edge preservation and smoothness of dehazed images, this paper proposed a fractional‐order gradient descent with momentum RBF neural network to optimize the nonlinear gradient domain‐guided filtering (NGDGIF‐FOGDMRBF). 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 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.