denoised

The experiment results show that the PSNR and vision of the curvelet denoised images improve a lot, especially at the edge of the image.

Noisy image can be denoised through this filter operator and wavelet thresholding technique.

Plenty of noises was denoised by the wavelet packet threshold denoising with local feature well in time-frequency domain.

The simulated signal and vibration signal of on-spot trolley track joints were denoised separately by wavelet threshold denoising.

Finally, the modified wavelet coefficients are transformed back into the original domain to get the denoised image.

The denoised image is obtained through inverse wavelet transform by the low frequency image and the high frequency images.

Comparing the waveform of denoised audio and noisy audio, it shows that the denoising result is perfect.

Firstly, reduced the dimensions of correlated inputs and denoised for sample by PLS, then construct the SVM model.

The filtered difference image is added back to the shrinkage image to get the final denoised image.

By applying the algorithm to the real SAR images, the denoised image obtains a pretty well visual effect and all indices are enhanced.

Next, these denoised images were fused using wavelet transform.

Each detail image is first denoised with ridgelet threshold method, and then decomposed with wavelet.

As decomposed scales ascend, d box of denoised signals descend, and keep stable at the end.

The simulation demonstrates that the new algorithms have a better denoised effect comparing with other traditional denoising methods.

Images were denoised by using several methods of thresholding.

Finally, inverse wavelet transform and inverse homomorphic transform were carried out and the denoised intensity image was obtained.

Method of process monitoring based on blind source separation with denoised information by wavelet transform