What is Wiener filtering in image processing?

What is Wiener filtering in image processing?

The Wiener filter is the MSE-optimal stationary linear filter for images degraded by additive noise and blurring. Calculation of the Wiener filter requires the assumption that the signal and noise processes are second-order stationary (in the random process sense).

What is meant by Wiener filtering?

In signal processing, the Wiener filter is a filter used to produce an estimate of a desired or target random process by linear time-invariant (LTI) filtering of an observed noisy process, assuming known stationary signal and noise spectra, and additive noise.

How Wiener filter is used for image restoration?

There is a technique known as Wiener filtering that is used in image restoration. This technique assumes that if noise is present in the system, then it is considered to be additive white Gaussian noise (AWGN). Observe that when K=0, the Wiener filter becomes the inverse filter. …

What are the applications of Wiener filtering?

Wiener filters play a central role in a wide range of applications such as linear prediction, echo cancellation, signal restoration, channel equalisation and system identification. The Wiener filter coefficients are calculated to minimise the average squared distance between the filter output and a desired signal.

Why Wiener filter is called optimum filter?

The general Wiener filtering problem can be stated as follows. A FIR filter whose output y[n] best approximates the desired signal s[n] in the sense that the mean square norm of the error is minimised is called the optimum FIR Wiener filter.

Why Wiener filter is used in image processing?

It removes the additive noise and inverts the blurring simultaneously. The Wiener filtering is optimal in terms of the mean square error. In other words, it minimizes the overall mean square error in the process of inverse filtering and noise smoothing. The Wiener filtering is a linear estimation of the original image.

How does Wiener filter work?

The Wiener filtering executes an optimal tradeoff between inverse filtering and noise smoothing. It removes the additive noise and inverts the blurring simultaneously. The Wiener filtering is optimal in terms of the mean square error.

Is Wiener filter an adaptive filter?

Wiener filter provides better performance for noise cancellation but it requires large no. Adaptive filter Fig 5 shows the basic adaptive filter with input signal and desired signal as inputs and one output signal with adaptive algorithm to adapt changes in the input signal.

How is the Wiener filter used in image processing?

The most important technique for removal of blur in images due to linear motion or unfocussed optics is the Wiener filter. From a signal processing standpoint, blurring due to linear motion in a photograph is the result of poor sampling.

Which is better Wiener filter or linear filter?

The Wiener filter tailors itself to the local image variance. Where the variance is large, wiener2 performs little smoothing. Where the variance is small, wiener2 performs more smoothing. This approach often produces better results than linear filtering.

What is the NSR of the Wiener filter?

By default, the Wiener restoration filter assumes the NSR is equal to 0. In this case, the Wiener restoration filter is equivalent to an ideal inverse filter, which can be extremely sensitive to noise in the input image. In this example, the noise in this restoration is amplified to such a degree that the image content is lost.

How is additive noise used in wiener2 filtering?

The additive noise (Gaussian white noise) power is assumed to be noise. The input image has been degraded by constant power additive noise. wiener2 uses a pixelwise adaptive Wiener method based on statistics estimated from a local neighborhood of each pixel.