| Summary: | 博士 === 國立交通大學 === 電子工程學系 電子研究所 === 101 === In this dissertation, an efficient Bayesian framework is proposed for image contrast enhancement. Starting from the image acquisition pipeline process, we model the image enhancement problem as a Maximum A Posteriori (MAP) estimation problem. The goal of MAP estimation is to infer a high-contrast image based on the observed low-contrast image and some well defined likelihood and prior models. In the proposed MAP inference framework, the likelihood model represents the relationship between the observed image and the desired image. On the other hand, the prior model describes some expected statistical properties of the desired high-contrast image. In the design of the likelihood model, we consider the correlations between low-contrast images and their corresponding high-contrast images. On the other hand, we design the prior model based on the observed image and some statistical properties of natural images. Since our framework has systematically considered several major factors that influence the quality of the acquired image, the proposed algorithm can effectively enhance the contrast level of the input image in a natural-looking way, while without producing apparent artifacts.
In the proposed MAP framework, a high-contrast image is derived by applying a suitable optimization solver to the aforementioned MAP estimation problem. However, it is extremely time-consuming to find the optimal solution due to the large number of unknown variables in the MAP problem. Take a 300 by 200 image as an example, the processing time could be longer than 10 minutes! This long processing time makes the proposed MAP-based algorithm impractical for typical image editing applications. Hence, in the second part of the dissertation, we discuss how to simplify the MAP estimation process to meet the requirement of practical applications. By assuming that the relationship between the desired image and the observed image can be modeled as an intensity mapping function, the dimensionality of the original MAP estimation problem is greatly reduced. Simulation results show that the simplified MAP-based approach can greatly reduce the computational complexity of the original MAP-based algorithm, while without causing apparent degradation of visual quality.
For the proposed algorithm, the selection of model parameters is important. In the dissertation, we also discuss the proper selection of parameters for image enhancement. In this issue, we try to describe the relationship between image contents and the selection of parameters. With the help of some statistical analyses, we could effectively choose suitable values of the parameters for contrast enhancement.
In summary, in this dissertation, we verify the feasibility of the proposed algorithm for image contrast enhancement. The proposed framework properly integrate various kinds of information into a unified inference process. Besides, we also discuss the simplification of the algorithm and selection of parameter in order to fit for practical applications. Simulations results have demonstrated the feasibility of the proposed framework in providing flexible and effective image contrast enhancement.
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