#### What Is Image Restoration?

Image restoration is the activity of taking a degenerate/loud image and assessing the spotless, unique image. Defilement may come in numerous structures, for example, movement obscure, commotion, and camera miss focus.[1] Image restoration is performed by switching the cycle that obscured the image and such is performed by imaging a point source and utilize the point source image, which is known as the Point Spread Function (PSF) to reestablish the image data lost to the obscuring cycle.
#### What is the purpose of image restoration?

During the acquisition phase of data, images are often degraded. Motion blurring, data loss due to sampling, camera miss focus, quantization effects, and different sources of noise can be involved in the degradation. Image reconstruction is aimed at estimating the original image from the corrupted data.
#### What is the difference between enhancing images and restoring images?

Image Enhancement:-A technique that seeks to enhance bad images so that they "look" better. Image Restoration: a method aimed at inverting established image-related degradation activities.
#### What are image degradation and restoration?

Image restoration is the process of recovering an image degraded by some understanding of the degradation function H and the term 711 additive noise (x, y). Degradation is thus modeled in the restoration and its inverse method is applied to recover the original image.
f gi
Degradation function /I
9(x. y
Degradation
Restoration filter
Restoration
Fig: Image Restoration and Image Degradation Model

##### The objective of image restoration:

Image restoration is intended to achieve an approximation of the original image f (x, y). Here we find the necessary restore filters through some knowledge of H and 71(x,y), so that the output image f(x,y) is as similar as possible to the original image f(x,y) as it is practically impossible (or quite difficult) to completely (or precisely) restore the original image.
##### Terminology:

• g(x , y) = degraded image

• f (x, y) = input or original image

• f (x, y) = recovered or restored image

• n(x , y) = additive noise term In spatial domain: g (x , y) = h(x , y) f (x, y) 77(x, y) where, ® represents convolution
##### In the frequency domain:

Once the Fourier transform of the above equation has been taken:
G(u, v)= H (u,v)F(u,v) N (u, v)
If the restore filter applied is R(u, v), then R (u, v)
(u, v) = R(u, v)[G(u, v)] R(u, v)
1(u, v)= R(u, v)H (u, v)F(u, v) + R(u, v)N(u) (u, v)
F(u, v) F(u and v) respectively (for restoration)
The opposite of the degradation function H(u, v) and the neglect of the noise word is the restoration filter R(u, v). Here, H(u, v) is linear and invariant of position.
#### How it works

The target of Image restoration strategies is to lessen commotion and recuperate goal misfortune Image handling procedures are performed either in the image area or the recurrence space. The most direct and customary strategy for image reclamation is DE convolution, which is acted in the recurrence space and subsequent to registering the Fourier change of both the image and the PSF and fix the goal misfortune brought about by the obscuring factors. This DE convolution method, as a result of its immediate reversal of the PSF which regularly has helpless framework condition number, enhances clamor and makes a blemished DE blurred image. Likewise, routinely the obscuring cycle is thought to move invariant. Consequently more refined strategies, for example, regularized DE blurring, have been created to bring to the table vigorous recuperation under various sorts of commotions and obscuring capacities. It is of 3 kinds: 1. Mathematical revision 2. Radiometric amendment 3. Commotion expulsion