Connexions

You are here: Home » Content » Image Restoration Basics
Content Actions
Lenses

What is a lens?

Lenses

A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual Connexions member, a community, or a respected organization.

This content is ...
Affiliated with (?)
This content is either by members of the organizations listed or about topics related to the organizations listed. Click each link to see a list of all content affiliated with the organization.

  • This module is included inLens: Rice University OpenCourseWare
    By: OpenCourseWare ConsortiumAs a part of collection:"Intro to Digital Signal Processing"

    Click the "Rice University OCW" link to see all content affiliated with them.

    Rice University OCW
Tags

(?)

These tags come from the endorsement, affiliation, and other lenses that include this content.

Image Restoration Basics

Module by: Robert Nowak

Summary: The module provides an introduction into the concepts of image restoration and filtering.

Image Restoration

In many applications (e.g., satellite imaging, medical imaging, astronomical imaging, poor-quality family portraits) the imaging system introduces a slight distortion. Often images are slightly blurred and image restoration aims at deblurring the image.
The blurring can usually be modeled as an LSI system with a given PSF hmn h m n .
FT.png
Figure 1: Fourier Transform (FT) relationship between the two functions.
The observed image is
gmn=hmn*fmn g m n h m n f m n (1)
Guv=HuvFuv G u v H u v F u v (2)
Fuv=GuvHuv F u v G u v H u v (3)
Example 1: Image Blurring 
Above we showed the equations for representing the common model for blurring an image. In Figure 2 we have an original image and a PSF function that we wish to apply to the image in order to model a basic blurred image.
camera.pngpsf.png
Subfigure 2.1
Subfigure 2.2
Figure 2
Once we apply the PSF to the original image, we receive our blurred image that is shown in Figure 3:
cam_blur.png
Figure 3

Frequency Domain Analysis

Example 1 looks at the original images in its typical form; however, it is often useful to look at our images and PSF in the frequency domain. In Figure 4, we take another look at the image blurring example above and look at how the images and results would appear in the frequency domain if we applied the fourier transforms.
cam_freq.png
Figure 4

Comments, questions, feedback, criticisms?

Send feedback