基于支持向量机的湍流退化图像加速复原算法

IMAGE ACCELERATION RESTORATION ALGORITHM FOR TURBULENCE-DEGRADED IMAGES BASED ON SUPPORT VECTOR MACHINE

由于湍流图像的退化原因十分复杂,现有图像复原算法很难在复原效率和复原质量间达到很好的平衡,为此提出了一种基于支持向量机的湍流退化图像加速复原算法.该算法通过设置方差阈值进行样本选择,舍弃了冗余信息、提高了样本质量;同时,对序列图像进行实时模型更新,加快了序列图像的复原速度.针对电弧风洞图像,将加速复原算法和原算法进行了比较.实验结果表明,加速算法的复原速度更快、复原效果也更好,它可以有效地解决湍流退化给图像带来的噪声和能量衰减问题,并能很好地校正湍流效应引起的模糊和抖动现象.

Because of the complicated mechanisms of atmospheric turbulence, it is difficult for existing image restoration algorithms to achieve a good balance between restoration efficiency and quality. A technique for the acceleration of support vector machine based image restoration algorithm was presented to solve this problem. In this algorithm, variance threshold was applied to assist sample selection. Since redundant information was discarded by means of the sample selection algo- rithm, training samples became more effective. Meanwhile, a real-time model updating algorithm was applied to speed up the restoration rate of serial images. Comparisons of the acceleration method with the previously proposed restoration algo-rithm on electric arc wind-tunnel images were provided. Experimental results show that the proposed acceleration method runs faster and performs better. It can effectively restore the turbulence-degraded images from strong noise, energy attenuation, blur and jitter.