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.
Highlights
Marine accidents caused by visibility account for a considerable proportion of marine meteorological accidents
Wu et al used the dark channel prior defogging algorithm to reduce the influence of cloud cover on target recognition and improve the recognition effect of marine ships under complex weather conditions [3]
Li et al proposed a method based on the prior fusion of retinex and dark channel to enhance the defogging of sea cucumber images [4]
Summary
Marine accidents caused by visibility account for a considerable proportion of marine meteorological accidents. Taking into account the above analysis, this paper proposed a fractional-order gradient descent with momentum RBF neural network to optimize gradient domain-guided filtering. Wang et al proposed a new fractional gradient descent learning algorithm for radial basis function neural networks [18]. This paper proposed fractional-order gradient descent with momentum method for training RBF neural network. The proposed method estimates the optimal parameters of nonlinear gradient domain-guided image filter. (1) A nonlinear gradient domain-guided image filtering algorithm was proposed (2) The optimal value of model parameters is proved (3) Fractional-order calculus is applied to gradient descent with momentum algorithm for training neural network. The new algorithm is used to adjust the weights of the neural network to improve its learning speed and performance for optimizing gradient domain-guided filtering (4) The convergence of the FOGDM-RBF is proved
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