Image contrast expand enhancement system based on fuzzy theory

In the current age of information explosion, the extraction of relevant data from a large information pool, which includes images, has become crucial. Because of the easy availability of imaging devices, millions of images are being added to the image pool every day. Current image contrast enhancement methods have some drawbacks. First, for images captured in extreme lighting conditions, such as backlit images and extremely bright or dark images, extant image contrast enhancement methods cannot retain the brightness distribution details of the original image, resulting in image distortion. Second, the methods can only achieve global contrast enhancement but not local contrast enhancement. Third, these methods cannot satisfy the Human Visual System mapping curve, resulting in nonsmooth or distorted images. In this study, a novel image expand enhancement system based on fuzzy theory is proposed. This system has two major features: (1) the expand model is designed such that the correlations between light intensity and the intensity of an image captured in this light are considered to achieve local contrast enhancement, and (2) an extreme case of images processing step that is capable of enhancing. The proposed model has a fusion framework; therefore, the image fusion model can represent the relationship between the original image and the expanded image. Next, the fusion model describes some expected statistical properties of the desired intensity of the expand image. Thus, the details of the original image are retained. Experimental results revealed that the proposed algorithm is capable of adaptively enhancing the contrast of the original image while simultaneously extruding the details of objects in the image. The resulting enhanced image can be effectively used for image information analysis and other image-processing tasks.