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Image quality measures for performance assessment of compression transforms.
Title
Image quality measures for performance assessment of compression transforms.
Creator
Caimi, F. M., Schmalz, Mark S., Ritter, G. X., Harbor Branch Oceanographic Institute
Date Issued
1998
PURL
http://purl.flvc.org/FCLA/DT/3180421
Subject Headings
Image compression, Image Quality
Format
Document (PDF)
Spatial coherence methods in undersea image formation and detection
Title
Spatial coherence methods in undersea image formation and detection.
Creator
Caimi, F. M., Bailey, B. C., Blatt, J. H., Harbor Branch Oceanographic Institute
Date Issued
1996
PURL
http://purl.flvc.org/FCLA/DT/3183697
Subject Headings
Underwater imaging systems, Image Quality
Format
Document (PDF)
Comparison and development of compression algorithms for AUV telemetry: Recent advancements
Title
Comparison and development of compression algorithms for AUV telemetry: Recent advancements.
Creator
Caimi, F. M., Kocak, D. M., Ritter, G. X., Schmalz, Mark S., Harbor Branch Oceanographic Institute
Date Issued
1998
PURL
http://purl.flvc.org/FCLA/DT/3338524
Subject Headings
Algorithms, Telemetry, Image Quality, Image compression
Format
Document (PDF)
Improved LLS imaging performance in scattering-dominant waters
Title
Improved LLS imaging performance in scattering-dominant waters.
Creator
Dalgleish, Fraser R., Caimi, F. M., Britton, W. B., Andren, C. F.
Date Issued
2009
PURL
http://purl.flvc.org/FCLA/DT/3183174
Subject Headings
Underwater imaging systems, Lidar, Image Quality
Format
Document (PDF)
Surface mapping and imaging using low power lasers
Title
Surface mapping and imaging using low power lasers.
Creator
Caimi, F. M., Bessios, Anthony G., Harbor Branch Oceanographic Institute
Date Issued
1994
PURL
http://purl.flvc.org/FCLA/DT/3183689
Subject Headings
Underwater imaging systems, High resolution imaging, Image Quality
Format
Document (PDF)
Environmental performance bounds for undersea pulsed laser serial imagers
Title
Environmental performance bounds for undersea pulsed laser serial imagers.
Creator
Dalgleish, Fraser R., Vuorenkoski, Anni K., Nootz, G., Ouyang, Bing, Caimi, F. M.
Date Issued
2013
PURL
http://purl.flvc.org/fau/fd/FA00007401
Subject Headings
Underwater imaging systems, Electrooptics, Lasers, Imaging systems--Image quality
Format
Document (PDF)
Experimental imaging performance evaluation for alternate configurations of undersea pulsed laser serial imagers
Title
Experimental imaging performance evaluation for alternate configurations of undersea pulsed laser serial imagers.
Creator
Dalgleish, Fraser R., Vuorenkoski, Anni K., Nootz, G., Ouyang, Bing, Caimi, F. M.
Date Issued
2011
PURL
http://purl.flvc.org/FCLA/DT/3340796
Subject Headings
Electrooptics, Underwater imaging systems, Image Quality, Scattering (Physics), Lasers
Format
Document (PDF)
Using color image processing techniques to improve the performance of content-based image retrieval systems
Title
Using color image processing techniques to improve the performance of content-based image retrieval systems.
Creator
Costa, Fabio Morais., Florida Atlantic University, Furht, Borko
Abstract/Description
A Content-Based Image Retrieval (CBIR) system is a mechanism intended to retrieve a particular image from a large image repository without resorting to any additional information about the image. Query-by-example (QBE) is a technique used by CBIR systems where an image is retrieved from the database based on an example given by the user. The effectiveness of a CBIR system can be measured by two main indicators: how close the retrieved results are to the desired image and how fast we got those...
Show moreA Content-Based Image Retrieval (CBIR) system is a mechanism intended to retrieve a particular image from a large image repository without resorting to any additional information about the image. Query-by-example (QBE) is a technique used by CBIR systems where an image is retrieved from the database based on an example given by the user. The effectiveness of a CBIR system can be measured by two main indicators: how close the retrieved results are to the desired image and how fast we got those results. In this thesis, we implement some classical image processing operations in order to improve the average rank of the desired image, and we also implement two object recognition techniques to improve the subjective quality of the best ranked images. Results of experiments show that the proposed system outperforms an equivalent CBIR system in QBE mode, both from the point of view of precision as well as recall.
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Date Issued
2001
PURL
http://purl.flvc.org/fcla/dt/12870
Subject Headings
Image processing--Digital techniques, Imaging systems--Image quality, Information storage and retrieval systems
Format
Document (PDF)
IMAGE QUALITY AND BEAUTY CLASSIFICATION USING DEEP LEARNING
Title
IMAGE QUALITY AND BEAUTY CLASSIFICATION USING DEEP LEARNING.
Creator
Golchubian, Arash, Nojoumian, Mehrdad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
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...
Show moreThe 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.
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Date Issued
2022
PURL
http://purl.flvc.org/fau/fd/FA00014029
Subject Headings
Deep learning (Machine learning), Computer vision, Aesthetics, Image Quality
Format
Document (PDF)
Perceptual methods for video coding
Title
Perceptual methods for video coding.
Creator
Adzic, Velibor, Kalva, Hari, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
The main goal of video coding algorithms is to achieve high compression efficiency while maintaining quality of the compressed signal at the highest level. Human visual system is the ultimate receiver of compressed signal and final judge of its quality. This dissertation presents work towards optimal video compression algorithm that is based on the characteristics of our visual system. Modeling phenomena such as backward temporal masking and motion masking we developed algorithms that are...
Show moreThe main goal of video coding algorithms is to achieve high compression efficiency while maintaining quality of the compressed signal at the highest level. Human visual system is the ultimate receiver of compressed signal and final judge of its quality. This dissertation presents work towards optimal video compression algorithm that is based on the characteristics of our visual system. Modeling phenomena such as backward temporal masking and motion masking we developed algorithms that are implemented in the state-of- the-art video encoders. Result of using our algorithms is visually lossless compression with improved efficiency, as verified by standard subjective quality and psychophysical tests. Savings in bitrate compared to the High Efficiency Video Coding / H.265 reference implementation are up to 45%.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004074, http://purl.flvc.org/fau/fd/FA00004074
Subject Headings
Algorithms, Coding theory, Digital coding -- Data processing, Imaging systems -- Image quality, Perception, Video processing -- Data processing
Format
Document (PDF)
Enhancement in Low-Dose Computed Tomography through Image Denoising Techniques: Wavelets and Deep Learning
Title
Enhancement in Low-Dose Computed Tomography through Image Denoising Techniques: Wavelets and Deep Learning.
Creator
Mohammadi Khoroushadi, Mohammad Sadegh, Leventouri, Theodora, Zhuang, Hanqi, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description
Reducing the amount of radiation in X-ray computed tomography has been an active area of research in the recent years. The reduction of radiation has the downside of degrading the quality of the CT scans by increasing the ratio of the noise. Therefore, some techniques must be utilized to enhance the quality of images. In this research, we approach the denoising problem using two class of algorithms and we reduce the noise in CT scans that have been acquired with 75% less dose to the patient...
Show moreReducing the amount of radiation in X-ray computed tomography has been an active area of research in the recent years. The reduction of radiation has the downside of degrading the quality of the CT scans by increasing the ratio of the noise. Therefore, some techniques must be utilized to enhance the quality of images. In this research, we approach the denoising problem using two class of algorithms and we reduce the noise in CT scans that have been acquired with 75% less dose to the patient compared to the normal dose scans. Initially, we implemented wavelet denoising to successfully reduce the noise in low-dose X-ray computed tomography (CT) images. The denoising was improved by finding the optimal threshold value instead of a non-optimal selected value. The mean structural similarity (MSSIM) index was used as the objective function for the optimization. The denoising performance of combinations of wavelet families, wavelet orders, decomposition levels, and thresholding methods were investigated. Results of this study have revealed the best combinations of wavelet orders and decomposition levels for low dose CT denoising. In addition, a new shrinkage function is proposed that provides better denoising results compared to the traditional ones without requiring a selected parameter. Alternatively, convolutional neural networks were employed using different architectures to resolve the same denoising problem. This new approach improved denoising even more in comparison to the wavelet denoising.
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Date Issued
2018
PURL
http://purl.flvc.org/fau/fd/FA00013115
Subject Headings
Tomography--Image quality, Wavelets (Mathematics), Deep learning, Tomography, X-Ray Computed
Format
Document (PDF)