Cell localization is an important area of medical image analysis, which is dedicated to predicting the precise location of cells in an image. The existing localization paradigm is to predict the density map using a Convolutional Neural Networks (CNN) model based on vanilla convolution and then process the density map using a local maximum search strategy to obtain the cell location and number information. However, there are three main problems in this paradigm: 1) CNN models based on vanilla convolution have difficulty in handling large variations in cell color; 2) The density map is difficult to provide accurate cell location information and ideal gradient information, and the information loss is more obvious in dense regions; 3) The post-processing strategy of density maps is susceptible to background noise and mutual interference between negative and positive cells. To tackle the above issues, we have made a comprehensive update of the existing paradigm, which consists of three parts: 1) A multi-scale gradient aggregation module based on difference convolution to effectively mitigate the challenge of large variations in cell color; 2) A new exponential distance transform map that provides accurate cell location information along with ideal gradient information for model learning; 3) A post-processing strategy named cell center localization strategy based on location maps that can significantly improve the localization performance. Extensive experiments on multiple datasets show that our approach can substantially improve cell localization and counting performance, establishing a new baseline for the cell localization task, and thereby increasing the efficiency of computer-aided diagnosis.