Combined Opportunity Cost and Image Classification for Non-Linear Image Enhancement

In this paper, it is shown that nonlinear image enhancement can be used to improve the quality of a blurred image by using the concept of opportunity cost with image classification. 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 image classification technique to decide the best combination of clipping and scaling parameters by the opportunity cost method for image enhancement. Experimental results show that the proposed opportunity cost method 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.