To address the issue of image blurring when the object size exceeds the depth of the field for the imaging system, we propose an image enhancement method based on clear region screening. First, the imaging system employs step capture to obtain multiple images, each focusing on different clear areas of the same target. Second, a combination of bilateral filtering and edge extraction algorithms is employed to weaken low-definition regions of images while retaining strong edge features. The combination of strong edges and regions mitigates the misleading effects of blurred regions near edges. A high-resolution region screening algorithm based on image-variance features is then introduced to select clear images of non-edge regions and match them with strong region-edge maps to obtain the final target images. The experimental results show that, compared to conventional image-enhancement algorithms, the clear regions of the image obtained by the proposed method exhibit minimal change and the blurred regions are effectivly enhanced. Compared with the histogram equalization, adaptive filtering, and Retinex algorithms, the proposed algorithm increases the information entropy, signal-to-noise ratio, structural similarity, and sharpness by an average of 4.1%, 21.3%, 36.0%, and 9.53% , respectively, while decreases the standard error by 23.3% on average.