Abstract: Proposed in this paper is an adaptive image enhancement method on the basis of nonsubsampled Contourlet transform (NSCT) ， fuzzy sets and artificial bee colony (ABC) optimization ， which helps improve the low contrast and definition of the image acquired in practical applications. In this method ， first ， an input image is decomposed into a low-frequency sub-band and several high-frequency sub-bands through NSCT. Secondly ，the coefficients of high-frequency sub-bands are enhanced according to Bayesian shrinkage threshold and nonlinear gain function ， while that of the low-frequency sub-band is enhanced by using the fuzzy enhancement method with its adaptability improved by fuzzy parameter optimization via ABC algorithm. Then ， for the purpose of reducing running time ， the entropy of low-frequency sub-band image is used as the fitness function of ABC algorithm and a random initializing strategy of inferior populations is introduced to improve ABC algorithm. The proposed enhancement method is finally employed to process three kinds of images of freshwater fish ， rail surface and grain pest ， and a comparison is made between the proposed method and three other similar enhancement methods in terms of subjective visual effect and such objective quantitative evaluation indices as contrast gain ， definition gain and entropy. Experimental results show that the proposed method is of the most
excellent visual effect because it helps obtain images with improved contrast and definition ， smooth edge and greater information amount ， which benefits further accurate image detection and recognition.

Key words: image enhancement, nonsubsampled Contourlet transform, fuzzy sets, artificial bee colony algo-rithm, Bayesian shrinkage threshold, nonlinear gain, adaptive enhancement