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Description
The quality of real-world visual content is typically impaired by many factors including image noise and blur. Detecting and analyzing these impairments are important steps for multiple computer vision tasks. This work focuses on perceptual-based locally adaptive noise and blur detection and their application to image restoration.
In the context of noise detection, this work proposes perceptual-based full-reference and no-reference objective image quality metrics by integrating perceptually weighted local noise into a probability summation model. Results are reported on both the LIVE and TID2008 databases. The proposed metrics achieve consistently a good performance across noise types and across databases as compared to many of the best very recent quality metrics. The proposed metrics are able to predict with high accuracy the relative amount of perceived noise in images of different content.
In the context of blur detection, existing approaches are either computationally costly or cannot perform reliably when dealing with the spatially-varying nature of the defocus blur. In addition, many existing approaches do not take human perception into account. This work proposes a blur detection algorithm that is capable of detecting and quantifying the level of spatially-varying blur by integrating directional edge spread calculation, probability of blur detection and local probability summation. The proposed method generates a blur map indicating the relative amount of perceived local blurriness. In order to detect the flat
ear flat regions that do not contribute to perceivable blur, a perceptual model based on the Just Noticeable Difference (JND) is further integrated in the proposed blur detection algorithm to generate perceptually significant blur maps. We compare our proposed method with six other state-of-the-art blur detection methods. Experimental results show that the proposed method performs the best both visually and quantitatively.
This work further investigates the application of the proposed blur detection methods to image deblurring. Two selective perceptual-based image deblurring frameworks are proposed, to improve the image deblurring results and to reduce the restoration artifacts. In addition, an edge-enhanced super resolution algorithm is proposed, and is shown to achieve better reconstructed results for the edge regions.
In the context of noise detection, this work proposes perceptual-based full-reference and no-reference objective image quality metrics by integrating perceptually weighted local noise into a probability summation model. Results are reported on both the LIVE and TID2008 databases. The proposed metrics achieve consistently a good performance across noise types and across databases as compared to many of the best very recent quality metrics. The proposed metrics are able to predict with high accuracy the relative amount of perceived noise in images of different content.
In the context of blur detection, existing approaches are either computationally costly or cannot perform reliably when dealing with the spatially-varying nature of the defocus blur. In addition, many existing approaches do not take human perception into account. This work proposes a blur detection algorithm that is capable of detecting and quantifying the level of spatially-varying blur by integrating directional edge spread calculation, probability of blur detection and local probability summation. The proposed method generates a blur map indicating the relative amount of perceived local blurriness. In order to detect the flat
ear flat regions that do not contribute to perceivable blur, a perceptual model based on the Just Noticeable Difference (JND) is further integrated in the proposed blur detection algorithm to generate perceptually significant blur maps. We compare our proposed method with six other state-of-the-art blur detection methods. Experimental results show that the proposed method performs the best both visually and quantitatively.
This work further investigates the application of the proposed blur detection methods to image deblurring. Two selective perceptual-based image deblurring frameworks are proposed, to improve the image deblurring results and to reduce the restoration artifacts. In addition, an edge-enhanced super resolution algorithm is proposed, and is shown to achieve better reconstructed results for the edge regions.
ContributorsZhu, Tong (Author) / Karam, Lina (Thesis advisor) / Li, Baoxin (Committee member) / Bliss, Daniel (Committee member) / Myint, Soe (Committee member) / Arizona State University (Publisher)
Created2016
Description
In the past decades, single-cell metabolic analysis has been playing a key role in understanding cellular heterogeneity, disease initiation, progression, and drug resistance. Therefore, it is critical to develop technologies for individual cellular metabolic analysis using various configurations of microfluidic devices. Compared to bulk-cell analysis which is widely used by reporting an averaged measurement, single-cell analysis is able to present the individual cellular responses to the external stimuli. Particularly, oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) are two key parameters to monitor heterogeneous metabolic profiles of cancer cells. To achieve multi-parameter metabolic measurements on single cells, several technical challenges need to be overcome: (1) low adhesion of soft materials micro-fabricated on glass surface for multiple-sensor deposition and single-cell immobilization, e.g. SU-8, KMPR, etc.; (2) high risk of using external mechanical forces to create hermetic seals between two rigid fused silica parts, even with compliance layers; (3) how to accomplish high-throughput for single-cell trapping, metabolic profiling and drug screening; (4) high process cost of micromachining on glass substrate and incapability of mass production.
In this dissertation, the development of microfabrication technologies is demonstrated to design reliable configurations for analyzing multiple metabolic parameters from single cells, including (1) improved KMPR/SU-8 microfabrication protocols for fabricating microwell arrays that can be integrated and sealed to 3 × 3 tri-color sensor arrays for OCR and ECAR measurements; (2) design and characterization of a microfluidic device enabling rapid single-cell trapping and hermetic sealing single cells and tri-color sensors within 10 × 10 hermetically sealed microchamber arrays; (3) exhibition of a low-cost microfluidic device based on plastics for single-cell metabolic multi-parameter profiling. Implementation of these improved microfabrication methods should address the aforementioned challenges and provide a high throughput and multi-parameter single cell metabolic analysis platform.
In this dissertation, the development of microfabrication technologies is demonstrated to design reliable configurations for analyzing multiple metabolic parameters from single cells, including (1) improved KMPR/SU-8 microfabrication protocols for fabricating microwell arrays that can be integrated and sealed to 3 × 3 tri-color sensor arrays for OCR and ECAR measurements; (2) design and characterization of a microfluidic device enabling rapid single-cell trapping and hermetic sealing single cells and tri-color sensors within 10 × 10 hermetically sealed microchamber arrays; (3) exhibition of a low-cost microfluidic device based on plastics for single-cell metabolic multi-parameter profiling. Implementation of these improved microfabrication methods should address the aforementioned challenges and provide a high throughput and multi-parameter single cell metabolic analysis platform.
ContributorsSong, Ganquan (Author) / Meldrum, Deirdre R. (Thesis advisor) / Goryll, Michael (Committee member) / Kelbauskas, Laimonas (Committee member) / Wang, Hong (Committee member) / Arizona State University (Publisher)
Created2017