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

In this paper, a novel fractional-order fusion model (FFM) is presented for low-light image enhancement. Existing image enhancement methods don’t adequately extract contents from low-light areas, suppress noise, and preserve naturalness. To solve these problems, the main contributions of this paper are using fractional-order mask and the fusion framework to enhance the low-light image. Firstly, the fractional mask is utilized to extract illumination from the input image. Secondly, image exposure adjusts to visible the dark regions. Finally, the fusion approach adopts the extracting of more hidden contents from dim areas. Depending on the experimental results, the fractional-order differential is much better for preserving the visual appearance as compared to traditional integer-order methods. The FFM works well for images having complex or normal low-light conditions. It also shows a trade-off among contrast improvement, detail enhancement, and preservation of the natural feel of the image. Experimental results reveal that the proposed model achieves promising results, and extracts more invisible contents in dark areas. The qualitative and quantitative comparison of several recent and advance state-of-the-art algorithms shows that the proposed model is robust and efficient.

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Variance Reduction in Low Light Image Enhancement Model

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d4723.119420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 139-142

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Processing ◽

Image Enhancement ◽

Variance Reduction ◽

Low Light ◽

Light Image ◽

Hidden Layer

In image processing, enhancement of images taken in low light is considered to be a tricky and intricate process, especially for the images captured at nighttime. It is because various factors of the image such as contrast, sharpness and color coordination should be handled simultaneously and effectively. To reduce the blurs or noises on the low-light images, many papers have contributed by proposing different techniques. One such technique addresses this problem using a pipeline neural network. Due to some irregularity in the working of the pipeline neural networks model [1], a hidden layer is added to the model which results in a decrease in irregularity.

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Low Light Image Enhancement using Convolutional Neural Network

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35787 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 3463-3472

Author(s):

Choundur Vishnu

Keyword(s):

Neural Network ◽

Image Quality ◽

Convolutional Neural Network ◽

Image Enhancement ◽

Scene Understanding ◽

Object Location ◽

Low Light ◽

Light Image ◽

Image Improvement

Great quality images and pictures are remarkable for some perceptions. Nonetheless, not each and every images are in acceptable features and quality as they are capture in non-identical light atmosphere. At the point when an image is capture in a low light state the pixel esteems are in a low-esteem range, which will cause image quality to decrease evidently. Since the entire image shows up dull, it's difficult to recognize items or surfaces clearly. Thus, it is vital to improve the nature of low-light images. Low light image enhancement is required in numerous PC vision undertakings for object location and scene understanding. In some cases there is a condition when image caught in low light consistently experience the ill effects of low difference and splendor which builds the trouble of resulting undeniable level undertaking in incredible degree. Low light image improvement utilizing convolutional neural network framework accepts dull or dark images as information and creates brilliant images as a yield without upsetting the substance of the image. So understanding the scene caught through image becomes simpler task.

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Ship Traffic Flow Prediction Based on Fractional Order Gradient Descent with Momentum for RBF Neural Network

Journal of Ship Research ◽

10.5957/josr.08190052 ◽

2020 ◽

pp. 1-8

Author(s):

Xue Han

Keyword(s):

Neural Network ◽

Traffic Flow ◽

Fractional Order ◽

Gradient Descent ◽

Traffic Accidents ◽

Rbf Neural Network ◽

Port Management ◽

Ship Traffic ◽

Maritime Traffic ◽

Marine Traffic

To prevent the maritime traffic accidents and make scientific decision, scientific and accurate prediction of the traffic flow is useful, which has often been made by neural network. The weight updating methods have played an important role in improving the performance of neural networks. To ameliorate the oscillating phenomenon in training radial basis function (RBF) neural network, a fractional order gradient descent (GD) with momentum method for updating the weights of RBF neural network (FOGDM-RBF) is proposed. Its convergence is proved. The new algorithm is used to predict vessel traffic flow at Xiamen Port. It performs stable and converges to zero as the iteration increases. The results verify the theoretical results of the proposed algorithm such as its monotonicity and convergence. The descending curve of error values by fractional order GDM is smoother than the GD and GDM method. Error analysis shows that the algorithm can effectively accelerate the convergence speed of the GD method and improve its performance with high accuracy and validity. The influence of fractional order, number of hidden layer neurons, tide peak hours, and ship size is analyzed and compared. 1. Introduction As the world shipping becomes more and more busy, the large ship traffic flow leads to frequent maritime traffic accidents, resulting in huge economic losses. Ship traffic flow is a basic system in marine traffic engineering and an important index to measure the construction of marine traffic infrastructure. Its prediction results can provide basis for formulating scientific Port management planning and ship navigation management. Therefore, to ensure the accuracy and rationality of ship traffic flow forecasting is of great significance for improving port infrastructure construction and formulating scientific port management strategies. Many advanced artificial intelligence optimization algorithms have been used for ship traffic flow forecasting, such as artificial neural network (Zhai 2013; Zhang 2015). Neural network can deal with complex nonlinear problems and has achieved some results. However, the neural network itself has some shortcomings, such as slow learning speed, easy to fall into the local extremum, learning and memory instability, etc.

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

Journal of Sensors ◽

10.1155/2021/8864906 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Qionglin Fang ◽

X. U. E. Han

Keyword(s):

Neural Network ◽

Fractional Order ◽

Gradient Descent ◽

Rbf Neural Network ◽

Similarity Index ◽

Image Filtering ◽

Image Dehazing ◽

Filtering Algorithm ◽

Gradient Domain ◽

Guided Image Filtering

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.

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