Conventional SIFT image feature extraction methods have difficulty in extracting features from the defocused blurred area of multi-focus images. As a result， common features between images are local and few， leading to poor accuracy in multi-focus image registration， which seriously affects the quality of subsequent image fusion and 3D reconstruction. Based on analyzing the uncertainty of feature extraction from the defocused blurred areas of images， a feature extraction method is proposed for the defocused blurred area of multi-focus images. First， features are extracted from the focused clear area of multi-focus images. Subsequently， the features in the corresponding defocused blurred area are extracted using optical flow tracking， thereby avoiding the uncertainty of directly extracting features from the defocused blurred area. Experimental results show that the proposed method displays good feature extraction ability and accuracy in the defocused blurred area， significantly increasing the number of features matches. Feature extraction error ranges between 0.03-0.39 pixels， which is better than the 0.21-1.71 pixels of existing methods. This indicates a reduction in the uncertainty of feature extraction from the defocused blurred area， making it suitable for multi-focus image registration.