Date of Award
2022
Document Type
Dissertation
Publication Status
Version of Record
Submission Date
October 2026
Thesis/Dissertation Advisor [Chair]
Nojoumian, Mehrdad
Abstract
The field of computer vision has grown by leaps and bounds in the past decade. The rapid advances can be largely attributed to advances made in the field of Artificial Neural Networks and more specifically can be attributed to the rapid advancement of Convolutional Neural Networks (CNN) and Deep Learning. One area that is of great interest to the research community at large is the ability to detect the quality of images in the sense of technical parameters such as blurriness, encoding artifacts, saturation, and lighting, as well as for its’ aesthetic appeal. The purpose of such a mechanism could be detecting and discarding noisy, blurry, dark, or over exposed images, as well as detecting images that would be considered beautiful by a majority of viewers. In this dissertation, the detection of various quality and aesthetic aspects of an image using CNNs is explored. This research produced two datasets that are manually labeled for quality issues such as blur, poor lighting, and digital noise, and for their aesthetic qualities, and Convolutional Neural Networks were designed and trained using these datasets. Lastly, two case studies were performed to show the real-world impact of this research to traffic sign detection and medical image diagnosis.
Extent
96 p.
Recommended Citation
Golchubian, Arash, "IMAGE QUALITY AND BEAUTY CLASSIFICATION USING DEEP LEARNING" (2022). Electronic Theses and Dissertations 2020 - Present. 969.
https://digitalcommons.fau.edu/etd_general/969
Identifier
FA00014029
Comments
Includes bibliography.|Dissertation (Ph.D.)--Florida Atlantic University, 2022.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.