The Optimization Approach of Image Enhancement by Neural Network
Date Issued
2006
Date
2006
Author(s)
Yi-Ming, Huang
DOI
zh-TW
Abstract
As the growth of technology, the product about digital image becomes more and more popular. People gradually pay much attention about the enhancement of image captured by the digital equipment. Though the sensor are well-designed, the captured image is still different from the sight of people in some way, and the image with bad luminance distribution will result in the disappearance of the chrominance information. In order to evaluate the image quality, we quantize it by a function. And then, the problem is how to optimize the system. If the analytical form of the system exists, the extreme value can be easily derived from the partial derivative of the function. If not, we have to take advantage of some optimization algorithm to find the extreme value. Traditional random-based optimization methods such as genetic algorithm and particle swarm optimization do not find the best score efficiently. We utilize the neural network to model the nonlinear relationship between the input and output and gradient information which will adaptively guide the system to the state of optimization in less iterations than GA and PSO. The performance of the proposed work is better than the two algorithms mentioned before.
Subjects
影像增強
影像品質評估
類神經網路
基因演算法
粒子群優演算法
image enhancement
image quality evaluation
neural network
genetic algorithm
particle swarm optimization
Type
thesis
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