Low light image enhancement based on modified Retinex optimized by fractional order gradient descent with momentum RBF neural network
| Autor: | Han Xue |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Momentum (technical analysis)
Smoothness (probability theory) Artificial neural network Computer Networks and Communications Computer science 020207 software engineering 02 engineering and technology Regularization (mathematics) Hardware and Architecture 0202 electrical engineering electronic engineering information engineering Media Technology Gradient descent Algorithm Software |
| Zdroj: | Multimedia Tools and Applications. 80:19057-19077 |
| ISSN: | 1573-7721 1380-7501 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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