【文件属性】：
文件名称：a robust kalman filter design for image restoration
文件大小：359KB
文件格式：PDF
更新时间：2013-05-29 15:50:16
kalman filter
对图像利用降阶卡尔曼滤波进行恢复
In image deconvolution or restoration using Kalman filter, the
image and blur models are required to be known for the
restoration process. Generally, the accuracy of the restoration
depends on the accuracy of the given models. Unfortunately, the
image and blur models are normally unknown in practice. To
solve the problem, an identification stage is employed to
estimate the image and blur models. However, the estimated
models are seldom accurate especially with the presence of noise
in the image. This paper presents a robust Kalman filter design
for image deconvolution that can accommodate the inaccuracy in
the estimated image and blur models. If the inaccuracy can be
modelled as addictive white Gaussian noise with a known
variance, it can be stochastically account for in the robust filter
design. In the simulation tests performed, the robust design
achieved improved accuracy in the image restoration even
though inaccurate image and blur models were used.