2026-09-19T16:03:00Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1178412026-06-16T18:51:59Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Jae S. Lim.
Fuller, Megan M. (Megan Marie)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-09-17T14:51:55Z
2018-09-17T14:51:55Z
2018
2018
http://hdl.handle.net/1721.1/117841
1052124009
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 115-122).
Image modeling is an important area of image processing. Good image models are useful, for example, in image restoration problems because they provide constraints that can be imposed on degraded images to retrieve better approximations of the original image. Many physical models of images are separable functions of position and wavelength, which means that images can be written as a color independent local average multiplied by a color dependent residual. We will present experimental results showing that this is the case in practice, and discuss the limitations of this model. We will also show that several commonly used observations in image processing follow from this model. Finally, we will demonstrate the results of imposing the model constraints in several image denoising problems and show that degraded images can be improved by imposing the model constraints.
by Megan Fuller.
Ph.D.
131 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Derivation, experimental verification, and applications of a new color image model
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="085eff7a-dcc2-442a-aa3d-79cd60adce67">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Derivation, experimental verification, and applications of a new color image model</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2018</PublicationDate>
<Authors>
<Author>
<DisplayName>Fuller, Megan M. (Megan Marie)</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>Image modeling is an important area of image processing. Good image models are useful, for example, in image restoration problems because they provide constraints that can be imposed on degraded images to retrieve better approximations of the original image. Many physical models of images are separable functions of position and wavelength, which means that images can be written as a color independent local average multiplied by a color dependent residual. We will present experimental results showing that this is the case in practice, and discuss the limitations of this model. We will also show that several commonly used observations in image processing follow from this model. Finally, we will demonstrate the results of imposing the model constraints in several image denoising problems and show that degraded images can be improved by imposing the model constraints.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>