Overview

An image is never an exact representation of the object under observation; it is always corrupted by degradations during acquisition and within the imaging system itself. These include noise, blurring and distortion. Image restoration removes or reduces these degradations. The point spread function (PSF) or the modulation transfer function (MTF) provides a complete, quantitative description of an imaging system and directly characterizes the image degradation within the system and can be used to restore the fine detail in images. The problem is more complicated if the image is also degraded by significant amounts of noise. Restoration techniques attempt to model the degradation and apply the inverse process to recover the original image. They are most effective when the point spread function or modulation transfer function is known and the nature of the blurring and noise are well understood. Geometric distortions can be reversed using inverse bilinear equations and gray-level interpolation.

Learning objectives

After reading this chapter you will be able to:

Image degradation

Images can be degraded by a number of different mechanisms, including noise, blurring and distortion. Noise is present because any imaging device must use a finite exposure (or integration) time, which introduces stochastic noise from the random arrival of photons. Optical imperfections and instrumentation noise (for example, thermal noise in CCD devices) result in more noise. Sampling causes noise due to aliasing of high-frequency signal components, and digitization produces quantization errors. Further noise can be introduced by communication errors and compression. Blurring is present in any imaging system which uses electromagnetic radiation (for example, visible light and x-rays).