Image denoising of coal dust based on fractional calculus adaptive algorithm
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摘要: 针对传统的煤尘图像滤噪方法迭代过程长、滤噪效果不理想、纹理保持能力差等问题,对现有的滤噪方法进行改进,建立了基于分数阶微分模型的自适应滤噪算法。改进算法对参数u的变化梯度进行调整,从整数阶扩展到分数阶;根据区域特征分别对算法中的各项参数进行自适应选择。实验结果表明,改进后的滤噪算法收敛速度快,迭代次数少,滤噪效果好,纹理保持能力强,且其检测滤噪效果能力的量化指标获得了很好的改善。
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关键词:
- 煤尘 /
- 图像滤噪 /
- 分数阶微分自适应算法 /
- 峰值信噪比 /
- 边缘保持指数
Abstract: In view of problem of long iteration process, unsatisfactory image denoising effect and poor texture retention capacity of traditional denoising method of coal dust image, the paper improved existing method and built an adaptive denoising algorithm based on fractional calculus model. The improved algorithm adjusts gradient of fractional order u from integer order to fractional order, and makes model parameters vary adaptively according to regional characteristics. The experimental results show that the improved denoising algorithm has fast convergence, fewer iterations, good denoising effect, and strong texture retention ability, while its quantitative indicators to measure noise effect are improved.-
Key words:
- coal dust /
- image denoising /
- fractional calculus adaptive algorithm /
- PSNR /
- EPI
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