## How do I restore an image in Matlab?

Image Restoration

- Read an Input Image.
- Defining a Blurr Filter.
- Degrade the Image Quality by applying any filtering (eg Gaussian Blur, Motion Blur)
- Addition of Minimal Random Noise to the degraded Image (using randn)
- Computing DFT of Degraded Image.
- Computing DFT of Filter (size equal to the image)

**What is Deconvwnr Matlab?**

Description. J = deconvwnr(I,PSF) restores image I that was degraded by convolution with a point-spread function PSF and possibly by additive noise. The algorithm is optimal in a sense of least mean square error between the estimated and the true image, and uses the correlation matrices of image and noise.

**Why do we use filters in image restoration?**

In image processing filters are mainly used to suppress either the high frequencies in the image, i.e. smoothing the image, or the low frequencies, i.e. enhancing or detecting edges in the image. An image can be filtered either in the frequency or in the spatial domain.

### Why do we use Wiener filter?

The Wiener filter can be used to filter out the noise from the corrupted signal to provide an estimate of the underlying signal of interest. The Wiener filter is based on a statistical approach, and a more statistical account of the theory is given in the minimum mean square error (MMSE) estimator article.

**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.

**How does Imfilter work in Matlab?**

The imfilter function computes the value of each output pixel using double-precision, floating-point arithmetic. If the result exceeds the range of the data type, then imfilter truncates the result to the allowed range of the data type. If it is an integer data type, then imfilter rounds fractional values.

#### What do you mean by Wiener filter?

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.

**What is the difference between image restoration and image enhancement?**

Image Enhancement: – A process which aims to improve bad images so they will “look” better. Image Restoration: – A process which aims to invert known degradation operations applied to images.

**What are the steps of image enhancement?**

The main techniques for the image enhancement include contrast stretching, slicing, histogram equalization, and some algorithms based on the retinex [5–11], etc. Of all these algorithms, the algorithm based on the retinex has acceptable results, but it will produce the “halo effect” and high time complexity.

## Where is Wiener filter used?

The Wiener filter has a variety of applications in signal processing, image processing, control systems, and digital communications.

**How image restoration is done using Wiener filter what is advantage of this technique?**

The approach of reducing one degradation at a time allows us to develop a restoration algorithm for each type of degradation and simply combine them. The Wiener filtering executes an optimal tradeoff between inverse filtering and noise smoothing. It removes the additive noise and inverts the blurring simultaneously.