A BP neural network model for image restoration based on area around pixel and edge information

Liu, Yizhe; Li, Chaofeng; Xu, Hongchen

doi:10.1109/iceceng.2011.6057933

Cited by 1 publication

(3 citation statements)

References 6 publications

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“…The final experimental results were analyzed to find out the relationship among the population size, the convergence accuracy, and time complexity of the algorithm. The parameters were set as follows: population size separate values (5,10,15,20); max is 150 times; optimization iterative step is valued by TFOA algorithm. The search interval is shown in Table 1.…”

Section: Effect Of Population Size On Algorithm Performancementioning

confidence: 99%

“…After 500 iterations of training, the optimization results for parameters and are 5.2 and 0.6231. In order to verify the image restoration effect of the algorithm in this paper, the support vector regression [15] and BP neural network algorithm [10] are selected to compare, and the final image restoration effect is shown in Figure 4 and Table 4.…”

Section: Image Restoration Analysis Of Lssvm-tfoamentioning

confidence: 99%

“…In 2011, Mukherjee et al used a Multilayered Quantum Backpropagation Neural Network for Image Restoration [7]; Sethy et al proposed an image restoration method based on BP neural network, which has achieved good results, but there is still room for improvement [8]. References [9,10] use fuzzy neural network model for image restoration and the image quality has been improved to some extent, but there is still room for optimization. Reference [11] proposes image restoration based on support vector regression (SVR), which improve convergence accuracy and achieves better restoration effect.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Real‐Time Image Restoration Algorithm in Edge Computing

et al. 2018

Wireless Communications and Mobile Computing

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Owning to the high processing complexity, the image restoration can only be processed offline and hardly be applied in the real-time production life. The development of edge computing provides a new solution for real-time image restoration. It can upload the original image to the edge node to process in real time and then return results to users immediately. However, the processing capacity of the edge node is still limited which requires a lightweight image restoration algorithm. A novel real-time image restoration algorithm is proposed in edge computing. Firstly, 10 classical functions are used to determine the population size and maximum iteration times of traction fruit fly optimization algorithm (TFOA). Secondly, TFOA is used to optimize the optimal parameters of least squares support vector regression (LSSVR) kernel function, and the error function of image restoration is taken as an adaptive function of TFOA. Thirdly, the LLSVR algorithm is used to restore the image. During the image restoration process, the training process is to establish a mapping relationship between the degraded image and the adjacent pixels of the original image. The relationship is established; the degraded image can be restored by using the mapping relationship. Through the comparison and analysis of experiments, the proposed method can meet the requirements of real-time image restoration, and the proposed algorithm can speed up the image restoration and improve the image quality.

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Section: Effect Of Population Size On Algorithm Performancementioning

confidence: 99%

Section: Image Restoration Analysis Of Lssvm-tfoamentioning

confidence: 99%