General design method for the filters based on the requirements and a filter design chart

Author(s):  
R. Ramiz ◽  
F. Gunes
2003 ◽  
Vol 39 (8) ◽  
pp. 695 ◽  
Author(s):  
G. Jovanovic-Dolecek ◽  
J. Diaz-Carmona

2011 ◽  
Vol 128-129 ◽  
pp. 181-184
Author(s):  
You Lian Zhu ◽  
Cheng Huang

Design of morphological filter greatly depends on morphological operations and structuring elements selection. A filter design method used median closing morphological operation is proposed to enhance the image denoising ability and the PSO algorithm is introduced for structural elements selecting. The method takes the peak value signal-to-noise ratio (PSNR) as the cost function and may adaptively build unit structuring elements with zero square matrix. Experimental results show the proposed method can effectively remove impulse noise from a noisy image, especially from a low signal-to-noise ratio (SNR) image; the noise reduction performance has obvious advantages than the other.


2012 ◽  
Vol 16 ◽  
pp. 359-364 ◽  
Author(s):  
Yongjie Han ◽  
Zhengyi Ren ◽  
Yongxiang Tong

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yifu Feng ◽  
Zhi-Min Li ◽  
Xiao-Heng Chang

This paper investigates the problem of H∞ filtering for class discrete-time Lipschitz nonlinear singular systems with measurement quantization. Assume that the system measurement output is quantized by a static, memoryless, and logarithmic quantizer before it is transmitted to the filter, while the quantizer errors can be treated as sector-bound uncertainties. The attention of this paper is focused on the design of a nonlinear quantized H∞ filter to mitigate quantization effects and ensure that the filtering error system is admissible (asymptotically stable, regular, and causal), while having a unique solution with a prescribed H∞ noise attenuation level. By introducing some slack variables and using the Lyapunov stability theory, some sufficient conditions for the existence of the nonlinear quantized H∞ filter are expressed in terms of linear matrix inequalities (LMIs). Finally, a numerical example is presented to demonstrate the effectiveness of the proposed quantized filter design method.


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