Adaptive algorithms for image enhancement based on non-linear filtering techniques and the fuzzy property plane

Non-linear filters have proved to be powerful tools in image analysis, enhancement and restoration. Filters such as the ranked order, median, alpha-trimmed mean, weighted median and the class of morphological filters have been investigated thoroughly and have been shown to be indispensable in certain image processing applications. Another interesting, though unrelated, research area has been the use of Fuzzy set theoretic operations in image processing. The underlying idea in employing the Fuzzy set theory is to employ fuzzy membership grades to represent image characteristics and features. This paper seeks to unify these two methodologies resulting in an interesting approach to image enhancement problems. The emphasis is on using non-linear operations to generate a local fuzzy property plane. This is then used as an adaptive filter based on local image characteristics. In this paper, we propose a novel localized filtering approach similar to non-linear filters, the point of departure being that we work in the fuzzy property plane rather than the image domain itself. For instance, we may realize a fuzzy implementation based on the median filter which traditionally uses actual pixel intensities, to generate fuzzy memberships instead. In our experiments, we consider a locally adaptive contrast enhancement of X-ray images typically having low contrast (low dynamic ranges). The results are compared with traditional enhancement techniques, such as histogram equalization. The results provide a better subjective quality as compared to other approaches as is also evident from the histogram distribution of the processed images.