Non-linear Image Enhancement Using Opportunity Costs

Wang, Lung-Jen; Huang, Ya-Chun

doi:10.1109/cicsyn.2010.72

Cited by 9 publications

(8 citation statements)

References 5 publications

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“…When a decision is made, one best choice is making and the other alternatives are dropping [6][7][8]. For more details on this discussion, see [13].…”

Section: Optimal Parameter Solution Using Opportunity Costmentioning

confidence: 99%

“…INTRODUCTION When a digital image is blurred, the image enhancement is an indispensable post-processing approach [1,2]. The purpose of image enhancement is to process a given blurred image so that the blurred image is more suitable for image analysis or visual quality [2,8]. In [4] and [5], an efficient nonlinear image enhancement scheme with both low-pass filter and nonlinear operator is developed in order to predict a high-frequency image used for enhancing the visual quality of blurred image.…”

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confidence: 99%

“…Opportunity cost is a well known theory in economics [6,7]. The principle of opportunity cost can be used in the area of image enhancement for deciding the enhancement parameters [8]. Furthermore, the authors in [8] developed an improved nonlinear image enhancement scheme with cubic B-spline filter [3,14] and the concept of opportunity cost to identify the most suitable combination of enhancement parameters.…”

mentioning

confidence: 99%

“…The principle of opportunity cost can be used in the area of image enhancement for deciding the enhancement parameters [8]. Furthermore, the authors in [8] developed an improved nonlinear image enhancement scheme with cubic B-spline filter [3,14] and the concept of opportunity cost to identify the most suitable combination of enhancement parameters. That is, the concept of opportunity cost can be used to analyse the combination of clipping and scaling parameters for the nonlinear image enhancement method.…”

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confidence: 99%

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A Study on Opportunity Cost with Classification for Non-Linear Image Enhancement

Wang¹,

Huang²

2013

IJCSAI

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Abstract-Opportunity cost is a wide known theory in economics. The principle of opportunity cost and an effective image classification are utilized to decide the most suitable combination of clipping and scaling parameters in the nonlinear image enhancement method for various blurred images. It is also shown that this new image classification and opportunity cost for the nonlinear image enhancement method obtains a better subjective and objective performance than other nonlinear image enhancement methods for image quality.

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“…When a decision is made, one best choice is making and the other alternatives are dropping [6][7][8]. For more details on this discussion, see [13].…”

Section: Optimal Parameter Solution Using Opportunity Costmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A Study on Opportunity Cost with Classification for Non-Linear Image Enhancement

Wang¹,

Huang²

2013

IJCSAI

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“…Somebody click image from common environment with elevated dynamic range include both dark and bright regions. Due to go outside in dynamic range of human eyes sensing, those image are not easy to distinguish by human eyes [7]. Image enhancement is a general approach to get enhanced quality of those images in terms of human visual observation.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Non-Linear Enhancement using Gamma Correction and Dynamic Restoration

Singh¹,

Banga²

2014

IJCA

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This paper has proposed a new integrated image enhancement algorithm by integrating non linear image enhancement technique with dynamic restoration. Image processing plays a vital role in visualization application. It improves the visibility of poor images. Different techniques have been proposed so far. To improve image quality image enhancement can selectively enhance and restrain some information about image. It is a method which decreases image noise, eliminate artifacts, and maintain details. Its purpose is to amplify certain image features for analysis, diagnosis and display. The overall objective of this paper is to propose an integrated technique which will integrate the nonlinear enhancement technique with the gamma correction and dynamic restoration technique. The proposed algorithm is implemented in MATLAB. Experimental results have shown quite significant results over the available methods.

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