Human eyes cannot notice low contrast objects in the image. Image contrast enhancement methods can make the unnoticed objects noticed, and human can detect and recognize the objects. In order to guide the design of enhancement methods, performance of enhancement methods for objects detection and recognition(ODR) should be valued. The existing performance evaluation methods evaluate image enhancement methods by calculating the increment of contrast or image information entropy. However, it is essentially an image information transmission process that human detect and recognize objects in the image, and image contrast enhancement can be viewed as a form of image coding. According to human visual properties, the transmission process of ODR information are modeled in this paper, and a performance evaluation method was proposed from the information theory of Shannon.
The paper is committed in local image enhancement. At first, the authors propose an adjacent pixel gray order-preserving principle. Adjacent pixel gray order-preserving principle is the basement of local enhancement method which ensures that there is no distortion in processed image. And then, the authors propose an iterative algorithm, which could stretch gray-scale difference of adjacent pixels in premise of not changing gray magnitude relationship between adjacent pixels. At last, the authors propose a totally reference image quality assessment method based on adjacent pixel gray order-preserving principle. According to this quality assessment method, the authors made a set of comparative experiments with local histogram equalization and method. Experimental results show that the proposed enhancement method can get higher score and provide better visual effects, fully demonstrating its effectiveness. According to this quality assessment method, the proposed method shows a good effectiveness, through experimental results and comparison with local histogram equalization method. Local contrast enhancement, adjacent pixel gray order-preserving principle, iterative algorithm, image quality assessment.