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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.