Browsing by Author "Jen, Tzu-Cheng"

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AN EFFICIENT BAYESIAN FRAMEWORK FOR IMAGE ENHANCEMENT WITH SPATIAL CONSIDERATION
(2010)
Jen, Tzu-Cheng
;
Wang, Sheng-Jyh
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
In this paper, a Bayesian framework is proposed for image enhancement. We model the image enhancement problem as a maximum a posteriori (MAP) estimation problem and the posteriori distribution function is formulated based on the local structures and local gradients of the given image. By solving the MAP estimation problem, image contrast gets properly enhanced while image noise gets suppressed at the same time. Moreover, since directly solving an MAP estimation problem is impractical for real-time applications, we further simplify the process to generate an intensity mapping function that achieves comparable performance in image enhancement. Simulation results have demonstrated the applicability of the proposed method in providing a flexible and efficient way for image enhancement.
Bayesian Structure-Preserving Image Contrast Enhancement and Its Simplification
(2012-06-01)
Jen, Tzu-Cheng
;
Wang, Sheng-Jyh
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
In this paper, an efficient Bayesian framework is proposed for image contrast enhancement. Based on the image acquisition pipeline, we model the image enhancement problem as a maximum a posteriori (MAP) estimation problem, where the posteriori probability is formulated based on the local information of the given image. In our framework, we express the likelihood model as a local image structure preserving constraint, where the overall effect of the shutter speed and camera response function is approximated as a linear transformation. On the other hand, we design the prior model based on the observed image and some statistical property of natural images. With the proposed framework, we can effectively enhance the contrast of the image in a natural-looking way, while with fewer artifacts at the same time. Moreover, in order to apply the proposed MAP formulation to typical enhancement problems, like image editing, we further convert the estimation process into an intensity mapping process, which can achieve comparable enhancement performance with a much lower computational complexity. Simulation results have demonstrated the feasibility of the proposed framework in providing flexible and effective contrast enhancement.
Generalized histogram equalization based on local characteristics
(2006)
Jen, Tzu-Cheng
;
Wang, Sheng-Jyh
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
Histogram Equalization (HE) and its variations have been widely used in image enhancement. Even though these approaches may enhance image contrast in an effective and efficient way, they usually face some undesired drawbacks, like loss of image details, noise amplification and over-enhancement. In this paper, we propose a generalized histogram equalization technique based on localized image analysis. Starting from designing two measures f(1) and f(2) to measure local characteristics around each pixel, the global statistics of these two local measures are then recorded into an extended histogram. Based on this extended histogram, we develop a procedure to generate suitable intensity transfer functions for various applications, like contrast enhancement and shadow enhancement. Experimental results show that the proposed algorithm provides a flexible and efficient way for image enhancement.
IMAGE ENHANCEMENT BASED ON QUADRATIC PROGRAMMING
(2008)
Jen, Tzu-Cheng
;
Wang, Sheng-Jyh
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
In this paper, a simple but efficient image enhancement algorithm is proposed. We model the image enhancement problem as a quadratic programming problem, and the cost function of the optimization problem is formulated based on local gradients of an image. By adjusting the definitions of gain function and weighting function in the cost function, we can derive various kinds of intensity transfer functions to fit for different applications. Simulation results for contrast enhancement and shadow enhancement have demonstrated the feasibility of this approach in providing a flexible and efficient mechanism for image enhancement.
IMAGE RECOLORIZATION FOR THE COLORBLIND
(2009)
Huang, Jia-Bin
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Chen, Chu-Song
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Jen, Tzu-Cheng
;
Wang, Sheng-Jyh
;
電子工程學系及電子研究所
In this paper, we propose a new re-coloring algorithm to enhance the accessibility for the color vision deficient (or colorblind). Compared to people with normal color vision, people with color vision deficiency (CVD) have difficulty in distinguishing between certain combinations of colors. This may hinder visual communication owing to the increasing use of colors in recent years. To address this problem, we re-color the image to preserve visual detail when perceived by people with CVD. We first extract the representing colors in an image. Then we find the optimal mapping to maintain the contrast between each pair of these representing colors. The proposed algorithm is image content dependent and completely automatic. Experimental results on natural images are illustrated to demonstrate the effectiveness of the proposed re-coloring algorithm.