Noise Removal With Filtering Techniques

Noise Removal With Filtering Techniques

Vijayakumari B.
Copyright: © 2021 |Pages: 24
DOI: 10.4018/978-1-7998-2795-5.ch006
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Abstract

An overview of the image noise models and the de-noising techniques available are presented here. Basically, filtering is one of the de-noising approaches that is normally performed in both spatial and frequency domains. Thus, this chapter focuses on these two approaches. Few filters like mean, median, sharpening, and adaptive median filter are discussed under spatial domain. In the frequency domain, as Butterworth filter suits better for images, Butterworth low pass, high pass, and band pass filters along with homomorphic filters are also analyzed. It also provides a comparative analysis of these approaches for both synthetic and medical images with some performance measures.
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Noise Models

Noise will convey some superfluous information. It may be reproduced in the images like some extra lines, black and white dots, images with checker board effect, smeared edges in the image, non-uniform illumination and blurred edges if suppose either the camera or object is moving. In order to do image analysis with noisy image care must be taken to understand the noise effect by modeling it with the prior knowledge gained about it. These unwanted artifacts are caused by the Charge Coupled Device and Complementary Metal Oxide Semiconductor sensors present in the camera. The noise models are analyzed with the points spreading function (PSF) and modulation transfer function (MTF), Probability density function (PDF) and Histogram. The figure 1 depicts some of the basic and standard noise models available.

Figure 1.

Various noise models

978-1-7998-2795-5.ch006.f01

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