Entropy and information energy for fuzzy sets
Fuzzy Sets and Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Digital Image Processing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Adaptive image contrast enhancement using generalizations of histogram equalization
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
Target detection via digital image processing is crucial to biomedical diagnosis and homeland security. Both image contrast enhancement and image segmentation are among the most practical approaches of image processing. Under conditions of improper illumination and unpleasant disturbances, adaptive image enhancement can be conducted, which adapts to the intensity distribution within an image. In trimulus color systems, each of three color components takes an independent role along with image processing procedures. To evaluate actual effects of image enhancement, some quantity measures should be taken into account instead of on a basis of intuition exclusively. In this article, new quantitative measures for trimulus color systems are proposed instead of the existing gray level ones so as to evaluate color image enhancement. Rather than the gray level measures, the corresponding three color component energy, entropy and relative entropy are employed to measure the effectiveness of adaptive image enhancement techniques. Images are selected such that the obscure essential objects will be identified within the image scope.