Study and comparison on histogram-based local image enhancement methods

Histogram-based image enhancement methods are widely used in image pre-processing, which are vital for the consequential image analysis, feature extraction and object recognition. This paper studies six adaptive methods which modify the conventional histogram equalization (HE) and further concern the local quality of the normalized images. The parameters for controlling the normalization effects are also defined and discussed. During this study, we also show the relation between these methods. Experiments are conducted to evaluate the performance of these methods and the results using various standard quantative measures are also given.