Contents I. Introduction
Image enhancement, which transforms digital images to enhance the visual information within, is a primary operation for almost all vision and image processing tasks in several areas such as computer vision, biomedical image analysis, forensic video/image analysis, remote sensing and fault detection [2], [4]. For example, in forensic video/image analysis tasks, surveillance videos have quite different qualities compared with other videos such as the videos for high quality entertainment or TV broadcasting. High quality entertainment or broadcasting videos are produced under controlled lighting environment, whereas surveillance videos for monitoring outdoor scenes are acquired under greatly varied lighting conditions depending on the weather and the time of the day. One of the common defects of surveillance videos is poor contrast resulting from reduced image brightness range. A routine examination of the histograms of the images from the videos reveals that some of the images contain relatively few levels of brightness, and some of the images have a type of histograms. In the type of histograms, a large span of the intensity range at one end is unused while the other end of the intensity scale is crowded with high frequency peaks [4], which is typically representative of improperly exposed images. The problem is how to approximate or reconstruct information that was lost because of the image having been captured under suboptimal aperture or exposure conditions. Enhancement transformation to modify the contrast of an image within a display's dynamic range is, therefore, required in order to reveal full information contents in the videos, e.g., for forensic investigations.
