To address the underperformance of feature-based image registration under situations with large affine deformation and similar targets, and reduce the time cost, an improved Descriptor-Nets (D-Nets) algorithm based on matching quality purification was proposed. The feature points were detected by Features From Accelerated Segment Test (FAST) algorithm initially, and then they were filtered according to Harris corner response function and meshing. Furthermore, on the basis of calculating the line-descriptor, a hash table and a vote were constructed, thus rough-matching pairs could be obtained. Eventually, mismatches were eliminated by the purification based on matching quality. Experiments were carried out on Mikolajczyk standard image data set of Oxford University. Results show that the proposed improved D-Nets algorithm has an average registration accuracy of 92.2% and an average time cost of 2.48 s under large variation of scale, parallax and light. Compared to Scale-Invariant Feature Transform (SIFT), Affine-SIFT (ASIFT), original D-Nets algorithms, the improved algorithm has a similar registration accuracy with the original algorithm but with up to 80 times speed boost, and it has the best robustness which significantly outperforms SIFT and ASIFT, which is practical for image registration applications.