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

Opportunity cost is a wide known theory in economics. In this paper, it is shown that the concept of opportunity cost with image classification can be used to improve the nonlinear image enhancement method for blurred images. However, one observes from computer simulation that the values of clipping and scaling parameters are quite different in image enhancement for various blurred images. Therefore, one aim of this paper is to develop an effective classification technique to decide the best combination of clipping and scaling parameters by using opportunity cost for image enhancement. Experimental results show that the opportunity cost with image classification for the nonlinear image enhancement achieves a better subjective and objective image quality performance than the method using the opportunity cost without image classification and other nonlinear image enhancement methods.