摘要
针对雾天图像的特点,提出了一种基于粒子群优化算法的BP人工神经网络的雾天图像复原算法。算法无需依据大气模型,它利用BP神经网络的自学习、自记忆和泛化能力,先用一组样本图像对网络进行训练,建立雾天图像与其对应的清晰图像之间的非线性映射关系,然后利用训练好的BP神经网络对待复原的雾天图像进行复原处理。实验表明该算法能有效地提高图像清晰度和对比度,视觉效果明显改善。
In this paper, a new method is proposed for image restoration for fog image based on the Partical Swarm Optimization BP neural network. It does not need atmospheric mode, and the nonlinear mapping relationship between the fog image and its clear image is established by training the BP neural network which has the ability of learning, remembrance and generalizing with a group of sample images. Then fog image which needs restoring could be restored by the trained neural network. The experimental results show that the restored images are clearer and have high contrast and the visual quality has been improved significantly.
出处
《微计算机信息》
2011年第2期165-167,共3页
Control & Automation
关键词
雾天图像
图像复原
神经网络
粒子群优化算法
Flog Image
Image restoration
Neural network
Partical Swarm Optimization
