基于最大图像熵Gamma校正估计的图像特征点检测和匹配方法OA北大核心CSTPCD

An image feature point detection and matching method based on maximum image entropy Gamma correction estimation is proposed.The contrast of the image is enhanced by a preprocessing algorithm,which is applied to the detection and matching of image feature points.In the preprocessing stage,the image is first normalized using a logarithmic function,and divided into bright and dark components according to a set threshold;then different parameters are adaptively selected for Gamma correction for the two components respectively,and the correction parameter that maximizes its entropy is determined to be the optimal parameter for each component;finally,the above parameters are applied to Gamma correction to generate a corrected map that corresponds to the original image bright and dark regions,and fusion is performed.Finally,the above parameters are applied to the Gamma correction to generate the corrected maps corresponding to the bright and dark regions of the original image,and fused to generate the enhanced image.The pre-processed image is subjected to feature point detection and matching experiments.The results show that compared with the unprocessed algorithm,HE algorithm and adaptive Gamma algorithm,the number of matches after feature point detection is increased by 85.7％,26.4％and 15.2％on dark images,and 59.4％,12.2％and 103.8％on bright images,respectively,and the matching effect is significantly improved.