18-06-2017, 11:28 PM
hello i need a matlab code for impulse noise detection and removal in power line communication systems. i tank you if help me.
|
matlab code for impulse noise detection and removal
|
hello i need a matlab code for impulse noise detection and removal in power line communication systems. i tank you if help me.
27-06-2017, 11:16 AM
Improved Impulse Noise Detector (IID) is presented for the Adaptive Mid range Switching Filter (ASWM). The idea behind the improved impulse noise detection scheme is based on the normalized absolute difference with in the filtering window, and then eliminate the detected pulse noise in damaged images using the ASWM filter. This detection scheme efficiently distinguishes noisy and noiseless pixels. A weighted median filter, based on the standard deviation within the filtering window, is used in ASWM filtering. Applying the absolute difference will distinguish the difference between a noise-free and noisy pixel more precisely. The proposed scheme results in the efficient detection of noisy pixels. Extensive simulation results show that the proposed scheme significantly outperforms in terms of PSNR and MAE than many other variant filter types for median impulse noise random value. More about the IID scheme provides better noise detection performance.
We propose an approach for the detection and elimination of impulsive noise in color images based on the Moran I (MI) statistic. The proposed method consists of detection and elimination components and is called the Moran M vector median filter (MIVMF). The detection module is able to determine whether a pixel is noise or noise free. If it is a noise pixel, the vector median filter (VMF) will be used to eliminate noise. This detection capability complies with the so-called "switching" mechanism, which only selects noisy pixels for removal. Therefore, this proposed filter will accelerate the processing time with the reduced number of vector calculations in the VMF due to this detection function. This type of detection is achieved with the MI index and the one-dimensional Laplace grain indication. We compared the proposed MIVMF with other well-developed medium vector filters in the literature. Our experimental results show that the proposed filter is not only faster in the filtering process but also efficient in eliminating random impulse noise with different levels of noise in color images. The MIVMF demonstrates a promising denoising result based on the peak-to-noise criteria and the metric structural similarity index. With the display of processed images, the MIVMF can avoid blurring the image, preserve edge details and achieve superior noise reduction.
|
« Next Oldest | Next Newest »
|
